Senior Software Engineer
Bristol-Myers Squibb
Senior Software Engineer
Job Description
Lead the design and implementation of Generative AI applications, including prompt engineering, fine-tuning, retrieval-augmented generation (RAG), and orchestration of LLM workflows across business domains. Collaborate closely with data scientists, MLOps, platform engineers, and product owners to deliver GenAI solutions aligned to real-world use cases. Own the technical roadmap, architecture decisions, and delivery of GenAI projects across the enterprise. Evaluate and integrate with cloud-native AI services (e.g., Amazon Bedrock, Azure OpenAI, Vertex AI) and open-source models. Champion model observability, governance, and ethical AI practices, ensuring safety, transparency, and compliance in all LLM deployments. Mentor and guide a team of software engineers and AI practitioners, fostering a culture of continuous learning and innovation. Drive reusability and scalability by building reusable components, APIs, and internal developer tools for LLM-powered workflows. Conduct code and design reviews, and lead agile ceremonies to ensure high-quality and timely delivery. Communicate technical strategies, progress, and risks to both technical and non-technical stakeholders, including leadership. Partner with other cloud developers, data engineers, AI/ML specialists, and data scientists to design, develop, and implement cloud native solutions. Collaborate with data engineers and data scientists to build and optimize data pipelines, ETL processes, and machine learning models in the cloud. Develop and maintain microservices, APIs, and serverless applications. Ensure the security, reliability, and performance of cloud infrastructure and applications by implementing best practices, monitoring tools, and automated testing. Participate in code reviews, architecture discussions, and agile development processes to ensure high-quality, maintainable, and scalable code. Troubleshoot and resolve issues related to cloud infrastructure, applications, and data pipelines. Continuously research and stay up-to-date with the latest cloud technologies, trends, and best practices to drive innovation and improve existing solutions Provide technical guidance and mentorship to junior developers and team members. Document and communicate technical designs, processes, and solutions to both technical and non-technical stakeholders. Supporting People with Disabilities Bachelor's or Master's degree in Computer Science, Engineering, or a related field. Minimum of 6 years of experience in development, with a focus on cloud computing. Strong knowledge of cloud platforms (e.g., AWS, Azure, Google Cloud) and services, including compute, storage, networking, and security. Strong programming skills in languages such as Python, Java, or Node.js Experience with AWS services, including EC2, S3, Lambda, RDS, DynamoDB, API Gateway, and CloudFormation. Knowledge of cloud architecture patterns, best practices, and security principles. Experience with containerization technologies, such as Docker and Kubernetes, is a plus. Familiarity with data pipeline and ETL tools, such as Apache NiFi, AWS Glue, or Apache Airflow is a plus. Experience leading initiatives related to continuous improvement or implementation of new technologies. Works independently on most deliverables Strong analytical and problem-solving skills, with the ability to develop creative solutions to complex problems. Ability to manage multiple projects and priorities in a fast-paced, dynamic environment.
Key Responsibilities
- Lead the design and implementation of Generative AI applications, including prompt engineering, fine-tuning, retrieval-augmented generation (RAG), and orchestration of LLM workflows across business domains
- Collaborate closely with data scientists, MLOps, platform engineers, and product owners to deliver GenAI solutions aligned to real-world use cases
- Own the technical roadmap, architecture decisions, and delivery of GenAI projects across the enterprise
- Evaluate and integrate with cloud-native AI services (e.g., Amazon Bedrock, Azure OpenAI, Vertex AI) and open-source models
- Champion model observability, governance, and ethical AI practices, ensuring safety, transparency, and compliance in all LLM deployments
- Mentor and guide a team of software engineers and AI practitioners, fostering a culture of continuous learning and innovation
- Drive reusability and scalability by building reusable components, APIs, and internal developer tools for LLM-powered workflows
- Conduct code and design reviews, and lead agile ceremonies to ensure high-quality and timely delivery
- Communicate technical strategies, progress, and risks to both technical and non-technical stakeholders, including leadership
- Partner with other cloud developers, data engineers, AI/ML specialists, and data scientists to design, develop, and implement cloud native solutions
- Collaborate with data engineers and data scientists to build and optimize data pipelines, ETL processes, and machine learning models in the cloud
- Develop and maintain microservices, APIs, and serverless applications
- Ensure the security, reliability, and performance of cloud infrastructure and applications by implementing best practices, monitoring tools, and automated testing
- Participate in code reviews, architecture discussions, and agile development processes to ensure high-quality, maintainable, and scalable code
- Troubleshoot and resolve issues related to cloud infrastructure, applications, and data pipelines
- Continuously research and stay up-to-date with the latest cloud technologies, trends, and best practices to drive innovation and improve existing solutions
- Provide technical guidance and mentorship to junior developers and team members
- Document and communicate technical designs, processes, and solutions to both technical and non-technical stakeholders
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field
- Minimum of 6 years of experience in development, with a focus on cloud computing
- Strong knowledge of cloud platforms (e.g., AWS, Azure, Google Cloud) and services, including compute, storage, networking, and security
- Strong programming skills in languages such as Python, Java, or Node.js
- Experience with AWS services, including EC2, S3, Lambda, RDS, DynamoDB, API Gateway, and CloudFormation
- Knowledge of cloud architecture patterns, best practices, and security principles
Preferred Qualifications
- Experience with containerization technologies, such as Docker and Kubernetes
- Familiarity with data pipeline and ETL tools, such as Apache NiFi, AWS Glue, or Apache Airflow
- Experience leading initiatives related to continuous improvement or implementation of new technologies
Skills Required
- Strong analytical and problem-solving skills, with the ability to develop creative solutions to complex problems
- Ability to manage multiple projects and priorities in a fast-paced, dynamic environment
- Works independently on most deliverables
Locations
- Hyderabad TS, India
Salary
Salary not disclosed
Estimated Salary Rangemedium confidence
2,500,000 - 4,500,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Strong analytical and problem-solving skills, with the ability to develop creative solutions to complex problemsintermediate
- Ability to manage multiple projects and priorities in a fast-paced, dynamic environmentintermediate
- Works independently on most deliverablesintermediate
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field (experience)
- Minimum of 6 years of experience in development, with a focus on cloud computing (experience)
- Strong knowledge of cloud platforms (e.g., AWS, Azure, Google Cloud) and services, including compute, storage, networking, and security (experience)
- Strong programming skills in languages such as Python, Java, or Node.js (experience)
- Experience with AWS services, including EC2, S3, Lambda, RDS, DynamoDB, API Gateway, and CloudFormation (experience)
- Knowledge of cloud architecture patterns, best practices, and security principles (experience)
Preferred Qualifications
- Experience with containerization technologies, such as Docker and Kubernetes (experience)
- Familiarity with data pipeline and ETL tools, such as Apache NiFi, AWS Glue, or Apache Airflow (experience)
- Experience leading initiatives related to continuous improvement or implementation of new technologies (experience)
Responsibilities
- Lead the design and implementation of Generative AI applications, including prompt engineering, fine-tuning, retrieval-augmented generation (RAG), and orchestration of LLM workflows across business domains
- Collaborate closely with data scientists, MLOps, platform engineers, and product owners to deliver GenAI solutions aligned to real-world use cases
- Own the technical roadmap, architecture decisions, and delivery of GenAI projects across the enterprise
- Evaluate and integrate with cloud-native AI services (e.g., Amazon Bedrock, Azure OpenAI, Vertex AI) and open-source models
- Champion model observability, governance, and ethical AI practices, ensuring safety, transparency, and compliance in all LLM deployments
- Mentor and guide a team of software engineers and AI practitioners, fostering a culture of continuous learning and innovation
- Drive reusability and scalability by building reusable components, APIs, and internal developer tools for LLM-powered workflows
- Conduct code and design reviews, and lead agile ceremonies to ensure high-quality and timely delivery
- Communicate technical strategies, progress, and risks to both technical and non-technical stakeholders, including leadership
- Partner with other cloud developers, data engineers, AI/ML specialists, and data scientists to design, develop, and implement cloud native solutions
- Collaborate with data engineers and data scientists to build and optimize data pipelines, ETL processes, and machine learning models in the cloud
- Develop and maintain microservices, APIs, and serverless applications
- Ensure the security, reliability, and performance of cloud infrastructure and applications by implementing best practices, monitoring tools, and automated testing
- Participate in code reviews, architecture discussions, and agile development processes to ensure high-quality, maintainable, and scalable code
- Troubleshoot and resolve issues related to cloud infrastructure, applications, and data pipelines
- Continuously research and stay up-to-date with the latest cloud technologies, trends, and best practices to drive innovation and improve existing solutions
- Provide technical guidance and mentorship to junior developers and team members
- Document and communicate technical designs, processes, and solutions to both technical and non-technical stakeholders
Tags & Categories
Senior Software Engineer
Bristol-Myers Squibb
Senior Software Engineer
Job Description
Lead the design and implementation of Generative AI applications, including prompt engineering, fine-tuning, retrieval-augmented generation (RAG), and orchestration of LLM workflows across business domains. Collaborate closely with data scientists, MLOps, platform engineers, and product owners to deliver GenAI solutions aligned to real-world use cases. Own the technical roadmap, architecture decisions, and delivery of GenAI projects across the enterprise. Evaluate and integrate with cloud-native AI services (e.g., Amazon Bedrock, Azure OpenAI, Vertex AI) and open-source models. Champion model observability, governance, and ethical AI practices, ensuring safety, transparency, and compliance in all LLM deployments. Mentor and guide a team of software engineers and AI practitioners, fostering a culture of continuous learning and innovation. Drive reusability and scalability by building reusable components, APIs, and internal developer tools for LLM-powered workflows. Conduct code and design reviews, and lead agile ceremonies to ensure high-quality and timely delivery. Communicate technical strategies, progress, and risks to both technical and non-technical stakeholders, including leadership. Partner with other cloud developers, data engineers, AI/ML specialists, and data scientists to design, develop, and implement cloud native solutions. Collaborate with data engineers and data scientists to build and optimize data pipelines, ETL processes, and machine learning models in the cloud. Develop and maintain microservices, APIs, and serverless applications. Ensure the security, reliability, and performance of cloud infrastructure and applications by implementing best practices, monitoring tools, and automated testing. Participate in code reviews, architecture discussions, and agile development processes to ensure high-quality, maintainable, and scalable code. Troubleshoot and resolve issues related to cloud infrastructure, applications, and data pipelines. Continuously research and stay up-to-date with the latest cloud technologies, trends, and best practices to drive innovation and improve existing solutions Provide technical guidance and mentorship to junior developers and team members. Document and communicate technical designs, processes, and solutions to both technical and non-technical stakeholders. Supporting People with Disabilities Bachelor's or Master's degree in Computer Science, Engineering, or a related field. Minimum of 6 years of experience in development, with a focus on cloud computing. Strong knowledge of cloud platforms (e.g., AWS, Azure, Google Cloud) and services, including compute, storage, networking, and security. Strong programming skills in languages such as Python, Java, or Node.js Experience with AWS services, including EC2, S3, Lambda, RDS, DynamoDB, API Gateway, and CloudFormation. Knowledge of cloud architecture patterns, best practices, and security principles. Experience with containerization technologies, such as Docker and Kubernetes, is a plus. Familiarity with data pipeline and ETL tools, such as Apache NiFi, AWS Glue, or Apache Airflow is a plus. Experience leading initiatives related to continuous improvement or implementation of new technologies. Works independently on most deliverables Strong analytical and problem-solving skills, with the ability to develop creative solutions to complex problems. Ability to manage multiple projects and priorities in a fast-paced, dynamic environment.
Key Responsibilities
- Lead the design and implementation of Generative AI applications, including prompt engineering, fine-tuning, retrieval-augmented generation (RAG), and orchestration of LLM workflows across business domains
- Collaborate closely with data scientists, MLOps, platform engineers, and product owners to deliver GenAI solutions aligned to real-world use cases
- Own the technical roadmap, architecture decisions, and delivery of GenAI projects across the enterprise
- Evaluate and integrate with cloud-native AI services (e.g., Amazon Bedrock, Azure OpenAI, Vertex AI) and open-source models
- Champion model observability, governance, and ethical AI practices, ensuring safety, transparency, and compliance in all LLM deployments
- Mentor and guide a team of software engineers and AI practitioners, fostering a culture of continuous learning and innovation
- Drive reusability and scalability by building reusable components, APIs, and internal developer tools for LLM-powered workflows
- Conduct code and design reviews, and lead agile ceremonies to ensure high-quality and timely delivery
- Communicate technical strategies, progress, and risks to both technical and non-technical stakeholders, including leadership
- Partner with other cloud developers, data engineers, AI/ML specialists, and data scientists to design, develop, and implement cloud native solutions
- Collaborate with data engineers and data scientists to build and optimize data pipelines, ETL processes, and machine learning models in the cloud
- Develop and maintain microservices, APIs, and serverless applications
- Ensure the security, reliability, and performance of cloud infrastructure and applications by implementing best practices, monitoring tools, and automated testing
- Participate in code reviews, architecture discussions, and agile development processes to ensure high-quality, maintainable, and scalable code
- Troubleshoot and resolve issues related to cloud infrastructure, applications, and data pipelines
- Continuously research and stay up-to-date with the latest cloud technologies, trends, and best practices to drive innovation and improve existing solutions
- Provide technical guidance and mentorship to junior developers and team members
- Document and communicate technical designs, processes, and solutions to both technical and non-technical stakeholders
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field
- Minimum of 6 years of experience in development, with a focus on cloud computing
- Strong knowledge of cloud platforms (e.g., AWS, Azure, Google Cloud) and services, including compute, storage, networking, and security
- Strong programming skills in languages such as Python, Java, or Node.js
- Experience with AWS services, including EC2, S3, Lambda, RDS, DynamoDB, API Gateway, and CloudFormation
- Knowledge of cloud architecture patterns, best practices, and security principles
Preferred Qualifications
- Experience with containerization technologies, such as Docker and Kubernetes
- Familiarity with data pipeline and ETL tools, such as Apache NiFi, AWS Glue, or Apache Airflow
- Experience leading initiatives related to continuous improvement or implementation of new technologies
Skills Required
- Strong analytical and problem-solving skills, with the ability to develop creative solutions to complex problems
- Ability to manage multiple projects and priorities in a fast-paced, dynamic environment
- Works independently on most deliverables
Locations
- Hyderabad TS, India
Salary
Salary not disclosed
Estimated Salary Rangemedium confidence
2,500,000 - 4,500,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Strong analytical and problem-solving skills, with the ability to develop creative solutions to complex problemsintermediate
- Ability to manage multiple projects and priorities in a fast-paced, dynamic environmentintermediate
- Works independently on most deliverablesintermediate
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field (experience)
- Minimum of 6 years of experience in development, with a focus on cloud computing (experience)
- Strong knowledge of cloud platforms (e.g., AWS, Azure, Google Cloud) and services, including compute, storage, networking, and security (experience)
- Strong programming skills in languages such as Python, Java, or Node.js (experience)
- Experience with AWS services, including EC2, S3, Lambda, RDS, DynamoDB, API Gateway, and CloudFormation (experience)
- Knowledge of cloud architecture patterns, best practices, and security principles (experience)
Preferred Qualifications
- Experience with containerization technologies, such as Docker and Kubernetes (experience)
- Familiarity with data pipeline and ETL tools, such as Apache NiFi, AWS Glue, or Apache Airflow (experience)
- Experience leading initiatives related to continuous improvement or implementation of new technologies (experience)
Responsibilities
- Lead the design and implementation of Generative AI applications, including prompt engineering, fine-tuning, retrieval-augmented generation (RAG), and orchestration of LLM workflows across business domains
- Collaborate closely with data scientists, MLOps, platform engineers, and product owners to deliver GenAI solutions aligned to real-world use cases
- Own the technical roadmap, architecture decisions, and delivery of GenAI projects across the enterprise
- Evaluate and integrate with cloud-native AI services (e.g., Amazon Bedrock, Azure OpenAI, Vertex AI) and open-source models
- Champion model observability, governance, and ethical AI practices, ensuring safety, transparency, and compliance in all LLM deployments
- Mentor and guide a team of software engineers and AI practitioners, fostering a culture of continuous learning and innovation
- Drive reusability and scalability by building reusable components, APIs, and internal developer tools for LLM-powered workflows
- Conduct code and design reviews, and lead agile ceremonies to ensure high-quality and timely delivery
- Communicate technical strategies, progress, and risks to both technical and non-technical stakeholders, including leadership
- Partner with other cloud developers, data engineers, AI/ML specialists, and data scientists to design, develop, and implement cloud native solutions
- Collaborate with data engineers and data scientists to build and optimize data pipelines, ETL processes, and machine learning models in the cloud
- Develop and maintain microservices, APIs, and serverless applications
- Ensure the security, reliability, and performance of cloud infrastructure and applications by implementing best practices, monitoring tools, and automated testing
- Participate in code reviews, architecture discussions, and agile development processes to ensure high-quality, maintainable, and scalable code
- Troubleshoot and resolve issues related to cloud infrastructure, applications, and data pipelines
- Continuously research and stay up-to-date with the latest cloud technologies, trends, and best practices to drive innovation and improve existing solutions
- Provide technical guidance and mentorship to junior developers and team members
- Document and communicate technical designs, processes, and solutions to both technical and non-technical stakeholders
Tags & Categories