Job Description : Gen AI Engineer

Experience Required : 5+ Years

Work Mode : Remote

About the Role :

We are looking for an experienced Gen AI Engineer to design, build, and deploy production-grade Generative AI and Agentic AI systems. The ideal candidate has hands-on experience across the full Gen AI stack - from LLM orchestration and RAG pipeline architecture to fine-tuning, deployment, and monitoring - and is comfortable working independently in a remote-first environment.

Key Responsibilities :

- Design, build, and deploy Retrieval-Augmented Generation (RAG) pipelines, including semantic chunking, embedding generation, and hybrid retrieval strategies.

- Architect and implement Agentic AI workflows using frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or Phidata for multi-step, tool-calling agent orchestration.

- Fine-tune and optimize LLMs using parameter-efficient techniques (PEFT, LoRA, QLoRA) for domain-specific use cases.

- Integrate LLM-powered applications (chatbots, copilots, automation agents) with vector databases (Pinecone, Qdrant, Neo4j, pgvector, Milvus, etc.) and graph databases where applicable.

- Build and maintain end-to-end AI pipelines (including on Databricks) for large-scale document ingestion, vectorization, and real-time inference.

- Deploy and manage AI systems on cloud platforms (AWS Bedrock/SageMaker, Azure OpenAI/AI Foundry, GCP Vertex AI) with secure, enterprise-grade configurations.

- Implement MLOps and CI/CD practices - containerization (Docker), orchestration (Kubernetes), experiment tracking (MLflow), and automated deployment pipelines (GitHub Actions).

- Monitor and evaluate production AI systems using tools such as LangSmith, RAGAS, DeepEval, or OpenTelemetry for reliability, drift detection, and performance tracking.

- Collaborate cross-functionally with product, data engineering, and (where applicable) front-end teams to integrate AI capabilities via REST APIs and microservices.

- Stay current with emerging Gen AI research, tools, and best practices, and evaluate their applicability to ongoing projects.

Required Skills & Experience:

- 5+ years of total IT experience, with significant hands-on experience in Generative AI / Agentic AI (typically 1.5 - 2+ years focused specifically on Gen AI).

- Strong programming proficiency in Python; working knowledge of SQL.

- Practical experience with Agentic AI frameworks: LangChain, LangGraph, CrewAI, AutoGen, Phidata, or similar.

- Experience with LLM providers: OpenAI, Anthropic (Claude), Google Gemini, Hugging Face models.

- Solid understanding of RAG architecture, embeddings, and semantic/hybrid search.

- Experience with vector and/or graph databases (Pinecone, Qdrant, Neo4j, Milvus, ChromaDB, FAISS, pgvector).

- Familiarity with LLM fine-tuning techniques (PEFT, LoRA, QLoRA).

- Working knowledge of cloud platforms (AWS, Azure, or GCP) - particularly their AI/ML services.

- Experience with Databricks for building end-to-end AI pipelines, large-scale data ingestion, and vectorization workflows.

- Experience with Docker, CI/CD pipelines, and basic MLOps practices.

- Strong analytical, problem-solving, and communication skills; ability to work independently in a remote setting.

Good to Have :

- Experience with computer vision (OpenCV, YOLO) or OCR pipelines.

- Familiarity with observability/evaluation tools (LangSmith, RAGAS, DeepEval, MLflow).

- Exposure to REST API/microservices integration (FastAPI, Node.js) for AI-powered web applications.

- Experience with Kubernetes for container orchestration.

- Prior experience in enterprise/regulated environments (secure LLM deployments, private endpoints, Key Vault, etc.).

- Relevant certifications (AWS, Azure AI, DeepLearning.AI, etc.).