Ai Ml Engineer

We are seeking AI/ML Engineers who bridge the gap between experimentation and robust, production-ready systems. From core predictive modeling to advanced GenAI (RAG, agentic workflows, fine-tuning) and MLOps, we are hiring across three experience tiers:

Open Bands & Profiles

Band 1: Associate AI/ML Engineer (1-3 Years)

• Focus: Solid ML foundations, strong Python/SQL, and growing deployment exposure.

• Key Requirements: Supervised/unsupervised learning, scikit-learn, PyTorch/TensorFlow, basic data extraction, hands-on exposure to LLM APIs, and foundational RAG concepts.

• Band 2: Mid-Senior AI/ML Engineer (3-6 Years)

• Focus: Independent ownership of end-to-end pipelines from raw data to monitored production endpoints.

• Key Requirements: Deep learning fine-tuning, production RAG pipelines (LangChain/LlamaIndex, vector DBs), MLOps (Docker, CI/CD, MLflow/W&B), cloud ML platforms (AWS/GCP/Azure), and data drift/monitoring systems.

• Band 3: Senior / Lead AI/ML Engineer (6-9 Years)

• Focus: Technical leadership, high-stakes system design, and specialized AI architectures.
• Key Requirements: Agentic workflows (multi-step reasoning/tool-use), custom model architectures, alignment/fine-tuning (RLHF/DPO, LoRA), distributed GPU training, scalable ML infrastructure, and AI governance/security.

Core Stack

• Languages: Python (primary), SQL
• Frameworks: PyTorch, TensorFlow, scikit-learn
• GenAI & NLP: LLM APIs, RAG, LangChain/LlamaIndex, Vector DBs, Fine-Tuning
• Data & MLOps: Spark, Docker, Kubernetes, MLflow, AWS/GCP/Azure ML

Specialization Tracks

We also welcome specialists in:

• GenAI / LLM & NLP (RAG, Agentic Systems, Fine-Tuning)
• Computer Vision (CNNs, Vision Transformers, Multi-Modal)
• MLOps / ML Infrastructure (Distributed Training, Kubernetes, Scalable Serving

Interested candidates please share your Resumes to [email protected]