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ADP · Hyderabad, Telangana, India

Generative AI Engineer (NP upto 30 days)

entry_levelfull timePosted 6 days ago
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Stack mentioned

generative-aiagentic-aimachine-learningllmprompt-engineeringragpythonlangchainllamaindexopenaiazurevector-databasesawss3ecseksdynamodbidentity-and-access-managementpineconeelasticsearch

Job ID - IND168205

GenAI Developer – Job Description:

Key Responsibilities & Requirements:

- 3-5 years of software development experience with hands-on expertise in Generative AI, Agentic AI, Machine Learning, or AI-powered application development.

- Strong understanding of Large Language Models (LLMs), Prompt Engineering, Retrieval-Augmented Generation (RAG), AI Agents, Multi-Agent Systems, and Conversational AI solutions.

- Proficiency in Python and experience with frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar Agentic AI frameworks.

- Hands-on experience with Amazon Bedrock, OpenAI/Azure OpenAI services, embeddings, vector databases, and LLM orchestration platforms.

- Experience designing and developing Agentic AI solutions, including autonomous workflows, tool calling, task planning, memory management, and multi-agent collaboration.

- Strong knowledge of AWS services, including Amazon Bedrock, Lambda, S3, API Gateway, ECS/EKS, DynamoDB, CloudWatch, IAM, and Step Functions.

- Experience building and deploying scalable, secure, and production-grade GenAI and Agentic AI applications on AWS.

- Familiarity with vector databases such as Pinecone, FAISS, Chroma, OpenSearch, or similar technologies.

- Strong understanding of REST APIs, Microservices, CI/CD pipelines, Git, Docker, and cloud-native development practices.

- Ability to work closely with business stakeholders and cross-functional teams to identify use cases and deliver AI-driven solutions.

- Excellent analytical, problem-solving, and communication skills with a passion for innovation and emerging AI technologies.

Preferred Skills:

- Experience implementing enterprise-scale RAG pipelines, AI Agents, Multi-Agent architectures, and workflow automation solutions.

- Exposure to LLMOps, model evaluation, prompt optimization, monitoring, governance, and Responsible AI practices.

- AWS Certifications (Developer Associate, Solutions Architect, AI Practitioner) are a plus.

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