Description :

Role : AI/ML Engineer (GenAI Specialist)

Experience : 3+ Years

Location : Bangalore (Immediate to 15 Days Joiners)

Industry : Technology / AI Research & Development

Education : B.E / B.Tech / M.Tech in Computer Science, AI, or Data Science.

Role Summary :

We are seeking a high-caliber AI/ML Engineer to join our core AI team in Bangalore.

In this role, you will act as a "Generative AI Architect," responsible for designing and deploying production-grade systems that leverage the latest advancements in Large Language Models (LLMs).

You will specialize in building Retrieval-Augmented Generation (RAG) pipelines and Agentic AI workflows to solve complex enterprise problems.

The ideal candidate is an expert in LangChain, possesses a deep understanding of vector databases, and can navigate the transition from experimental notebooks to scalable, automated AI agents.

Responsibilities :

- Generative AI System Design : Architect and implement end-to-end GenAI solutions using state-of-the-art LLMs (GPT-4, Claude, Llama 3) for diverse enterprise use cases.

- Advanced RAG Implementation : Design and optimize Retrieval-Augmented Generation (RAG) pipelines, focusing on advanced indexing, chunking strategies, and re-ranking to improve response accuracy.

- Agentic AI Development : Build autonomous and semi-autonomous Agentic AI workflows that can reason, use tools, and execute multi-step tasks independently.

- Orchestration with LangChain : Leverage LangChain or LangGraph to manage complex LLM chains, memory states, and integration with external APIs and data sources.

- Vector Database Management : Manage and optimize high-dimensional data storage and retrieval using vector databases such as Pinecone, Milvus, Weaviate, or Qdrant.

- Prompt Engineering & Tuning : Develop and refine sophisticated prompt templates and explore techniques like Few-shot prompting or Chain-of-Thought (CoT) to enhance model performance.

- Fine-Tuning & Model Optimization : Participate in the fine-tuning of open-source models using techniques like LoRA or QLoRA to adapt LLMs to specific domains or tasks.

- MLOps & Deployment : Collaborate with engineering teams to deploy AI models into production environments using Docker and cloud-native services (AWS/Azure/GCP).

- Evaluation & Guardrails : Implement robust evaluation frameworks (e.g., RAGAS) and safety guardrails to monitor model hallucinations, bias, and performance metrics like faithfulness and relevancy.

- Rapid Prototyping : Quickly build and iterate on AI prototypes to meet the demands of a fast-paced development cycle, adhering to the 15-day joining timeline.

Technical Requirements :

- AI/ML Foundation : 3+ years of professional experience in machine learning, with a strong focus on Natural Language Processing (NLP).

- GenAI Mastery : Proven hands-on experience with LLMs, RAG, and Agentic Frameworks.

- Programming Proficiency : Expert-level skills in Python and familiarity with libraries like PyTorch or TensorFlow.

- Orchestration Expertise : Deep knowledge of LangChain, LangGraph, or similar orchestration frameworks.

- Data & Search : Experience with Vector Databases and semantic search implementation.

Preferred Skills :

- Multi-Agent Systems : Experience with CrewAI or AutoGPT for coordinating multiple AI agents.

- Evaluation Frameworks : Familiarity with tools like LangSmith or TruLens for model observability.

- Cloud Platforms : Experience with AWS Bedrock, Azure OpenAI Service, or GCP Vertex AI.

Core Competencies :

- Result Driven : Ability to deliver production-ready AI components within tight deadlines (Immediate to 15 days).

- Problem Solving : A relentless drive to solve high-dimensional data challenges and minimize model hallucinations.

- Innovation Mindset : Staying updated with the daily evolution of the Generative AI landscape.

- Collaborative Leadership : Strong communication skills to work with cross-functional product and engineering squads