AI Engineer - GenAI/LLM
First seen by Alion on Aug 8, 2026.
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).
Skills
Machine Learning, Artificial Intelligence, Generative AI, LLM, RAG, Python, Deep Learning, NLP, REST API, VectorDB, MLOps