GenAI/ML Engineers

Job Summary

GenAI/ML Engineers

Location: Hyderabad / Noida / Gurgaon

Responsibilities

• Demonstrated experience working across the full AI/ML project lifecycle, from business requirements analysis to model deployment and productization.
• Practical experience using AI-assisted engineering tools such as GitHub Copilot or similar capabilities to improve productivity, code quality, documentation, testing, and development efficiency.
• Strong understanding of responsible AI usage in software and AI/ML engineering, including validation of AI-generated outputs, secure coding practices, data privacy, explainability, and adherence to enterprise standards.
• Strong analytical and problem-solving skills, with a structured approach to debugging, model evaluation, performance tuning, and optimization.
• Excellent communication and collaboration abilities, with experience working in cross-functional and agile teams.
• Ability to translate complex business problems into technical challenges that can be solved using data science, machine learning, and AI techniques.
• Ability to communicate technical ideas, model outcomes, and AI/ML solution approaches effectively to non-technical audiences.
• Ability to work effectively in a team-oriented environment.
• Curious and open-minded attitude toward new approaches, technologies, and methodologies.
• Strong ownership mindset with the ability to drive initiatives from concept through delivery.

Requirements

• Bachelors or masters degree in Computer Science, Data Science, or a related technical discipline.
• 10+ years of overall software development experience, including 3+ years of hands-on experience in machine learning or AI application development, with a proven track record of delivering production-grade solutions.
• Strong programming proficiency in Python, with demonstrated experience in API development using FastAPI or Flask.
• Backend development experience in C# or Java is considered an added advantage.
• Solid experience developing AI solutions within the AWS ecosystem, including hands-on use of services such as Amazon Bedrock, SageMaker, Lambda, API Gateway, and S3.
• Practical knowledge and implementation experience with Large Language Models and Generative AI frameworks such as LangChain, LlamaIndex, or custom RAG architectures.
• In-depth understanding of RAG systems, including embedding generation, document chunking, retrieval, ranking, and response synthesis.
• Experience with vector databases such as Pinecone, FAISS, or OpenSearch, as well as text embeddings and prompt engineering.
• Strong understanding of cloud security, including OAuth 2.0-based authentication, IAM policies, secure API design, and data protection practices.
• Familiarity with DevOps practices and tools, including CI/CD pipelines, Docker, and Infrastructure as Code tools such as CloudFormation, AWS CDK, or Terraform.
• Proficiency in relational databases such as SQL and strong understanding of data modelling.
• Solid grasp of machine learning algorithms and techniques, including deep learning architectures.
• Skilled in data preprocessing, feature engineering, and data manipulation using modern data science tools and libraries.