AI Engineer

• Support use cases across multiple business domains.

• Own the end-to-end product lifecycle forGenerative AI, Agentic AI,AI/ML solutions, from design to deployment.

• Act as a bridge between business and technology teams, ensuring alignment of requirements and technical specifications.

• Enable data-driven insights and AI-powered automation for Conversational AI, GenAI, and ML use cases.

Key Responsibilities 

• Design & ImplementAgentic,GenAI Solutions:Build robustGenerative AIandAgentic AIapplications usingAWS Bedrock,SageMaker, and other AWS ML services.

• DevelopLLMbases UseCases:Architect and implementLLM based use cases like RAG, Summarization, data analysis etc.for enterprise-scale applications.

• LLMFinetuning& Evaluation:Fine-tune, evaluate, and optimizeLarge Language Models (LLMs)for performance, accuracy, and safety.

• Conversational AI Expertise:Design and deploy conversational AI models usingAmazon Lex,Amazon Connect, and custom NLP pipelines.

• Integration & Collaboration:Work closely with product managers, engineers, and UX teams to embed AI capabilities into business workflows.

• Innovation & Research:Stay ahead of AI trends, frameworks, and best practices; apply them to drive continuous innovation.

• Quality & Governance:Maintain system design integrity, review test strategies, and ensure compliance with AI ethics and security standards.

• Incident Support:Assist in diagnosing and resolving production issues.

• Optimize solutions through thorough research experimentation, and advanced problem-solving techniques.

• Communicate complex concepts and results effectively to both technical and non-technical stakeholders.

Required Skills & Qualifications 

• Technical Expertise:

• Proficiency withdevelopingsolutionon AWS platform 

• Strong proficiency inPythonaround Agentic, GenAI andAI/ML libraries (LangChain,LangGraph,Google ADK,Langfuse, OTEL,Transformers,CrewAIetc.).

• Hands-on experience withAWS ML ecosystem(Bedrock,AgentCore,SageMaker, Lambda, API Gateway, EKS, Docker).

• Deep understanding ofAI Agents, Agentic architecture,Agentic Memory,Generative AI,LLMs,NLP, andConversational AI.

• Deep understandingonPrompt engineering and Prompt management, refining and optimizing prompts to enhance the outcomes of Large Language Models (LLMs)

• Experiencewith SQL, NoSQL andvector databases(e.g.,DynamoDB,PGVector,CromaDB, FAISSetc.)

• Experience withdata chunking and embeddingalong with expertise using differentembedding models.

• AI/ML Fundamentals:

• Solid grasp ofML algorithms, model evaluation techniques,  

• Hands onexpertise withmodel training, deployment and monitoring. 

• Additional Skills:

• Knowledge ofREST APIs,AWS CloudFormation, AWS Service catalog products,JSON/XML, CI/CD tools (Jenkins/Gitlab/Harness), and cloud-native architectures.

• Strong communication and stakeholder management skills.

• Ability to lead technical teams and mentor junior developers.