AI Engineer - GenAI/LLM

Job Details :

Role : AI Engineer (Python, GenAI/LLMs + ML Fundamentals)

Experience : 5+ Years

Location : Bangalore, L2-F2F

Work Mode : Work From Office (5 Days)

Notice Period : Immediate Joiners or Candidates Serving Notice Period Preferred

Employment Type : Full-Time

Key Responsibilities :

• Drive the complete machine learning lifecycle, including data curation, model development, evaluation, deployment, monitoring, and retraining for predictive and Generative AI systems.

• Build and maintain production-grade MLOps pipelines, including CI/CD, model registry, automated retraining, A/B testing, and drift detection.

• Fine-tune and deploy Large Language Models (LLMs) using techniques such as LoRA, QLoRA, PEFT, RLHF, and DPO.

• Design and implement Agentic AI solutions, including RAG pipelines, tool/function calling, memory management, planning loops, and multi-agent orchestration.

• Build evaluation frameworks to improve model quality, reduce hallucinations, and ensure trustworthy AI systems.

• Optimize inference performance for latency, throughput, scalability, and cost.

• Collaborate with engineering, product, and data teams to deliver enterprise AI solutions.

Required Skills :

• 5+ years of experience in AI/ML Engineering or Applied AI.

• Strong programming skills in Python.

• Hands-on experience with Generative AI, LLMs, and Retrieval-Augmented Generation (RAG).

• Experience with Agentic AI frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, or AutoGen.

• Strong understanding of Deep Learning, NLP, Transformers, embeddings, and PyTorch.

• Hands-on experience with the Hugging Face ecosystem (Transformers, PEFT, TRL, Accelerate).

• Experience with vector databases (Pinecone, Weaviate, Qdrant, pgvector, or FAISS).

• Experience with FastAPI, REST API development, and MLOps tools (MLflow, Airflow, Kubeflow, or Weights & Biases).

• Experience with Docker, Kubernetes, CI/CD, and cloud platforms (AWS, Azure, or GCP).

• Familiarity with inference optimization tools like vLLM, Triton, or TGI is an advantage.

Education :

• B.E./B.Tech/M.E./M.Tech in Computer Science, AI/ML, Data Science, or a related field (or equivalent practical experience with production AI deployments).