About the Role

We are seeking an enthusiastic AI/ML Engineer with strong skills in Python, Generative AI frameworks, and scalable cloud deployment. You will work on cutting-edge LLMs and open-source AI models, fine-tuning them for enterprise applications and deploying them seamlessly on AWS and containerized environments.

This role is ideal for freshers with strong project work as well as early-career professionals who are eager to grow in LLM development, MLOps, and enterprise AI deployments.

Roles & Responsibilities

Develop and deploy scalable APIs/microservices using Flask and FastAPI.
Work with state-of-the-art LLMs (Gemini, GPT-4/4o, LLaMA 3, Mistral, Mixtral, Falcon, Claude) and integrate them into business applications.
Fine-tune and optimize open-source LLMs (LLaMA, Mistral, Falcon, etc.) for on-premise enterprise use cases.
Implement RAG (Retrieval Augmented Generation) workflows using LangChain, LangGraph, and vector databases.
Design and manage containerized and serverless deployments on AWS (EC2, S3, ECR, Lambda, API Gateway) with Docker.
Collaborate with cross-functional teams to integrate AI services into production systems.
Ensure performance, scalability, and cost optimization of deployed AI solutions.
Stay updated with trends in Generative AI, multi-modal models, and AI infrastructure.

Qualifications Required

Must-Have Skills:

Strong programming foundation in Python.
Experience in building APIs using Flask and FastAPI.
Knowledge of LLM frameworks: Hugging Face Transformers, LangChain, LangGraph.
Familiarity with leading LLMs: Google Gemini, OpenAI GPT series, Meta LLaMA, Mistral/Mixtral, Falcon, Claude.
Cloud expertise with AWS services (EC2, S3, ECR, Lambda, API Gateway).
Experience with containerization (Docker) and version control (Git/GitHub).
Exposure to Elasticsearch for indexing, search, and RAG workflows.

Nice-to-Have (Added Advantage):

Experience fine-tuning open-source LLMs (LLaMA, Mistral, Falcon, etc.) for on-prem deployments.
Familiarity with vector databases (Pinecone, Weaviate, FAISS, Milvus).
Understanding of MLOps tools (MLflow, DVC, SageMaker) for CI/CD in AI.
Exposure to multi-modal models (text + image, text + audio) like Gemini Pro, LLaVA, GPT-4o.
Database knowledge: PostgreSQL, MongoDB, or NoSQL stores.
Strong grasp of data structures, algorithms, and system design.

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