AI / Machine Learning Engineer

we are looking for an AI/Machine Learning Engineer who can take models from a research notebook to a live, production-grade system that real users depend on. This is not a research-only role you'll spend as much time on deployment, monitoring, and scaling as you do on model architecture. You'll work at the intersection of data engineering, software engineering, and applied AI, embedded in a cross-functional team that ships AI-powered features on a regular release cycle.

You will own the full lifecycle of machine learning systems: understanding the business problem, framing it as an ML/AI task, sourcing and preparing data, building and validating models, deploying them into production, and then continuously monitoring and improving them based on real-world performance. Increasingly, this also means working with large language models — fine-tuning, retrieval-augmented generation (RAG), prompt optimization, and evaluating when a generative AI approach is the right tool versus a traditional ML model.

Roles & Responsibilities

Model Development

• Design, train, and validate machine learning and deep learning models for classification, regression, recommendation, NLP, or computer vision use cases depending on team focus.
• Build and fine-tune LLM-based solutions using techniques such as RAG, prompt engineering, and parameter-efficient fine-tuning (LoRA/PEFT).
• Run structured experiments, track metrics rigorously, and choose models based on defensible evaluation criteria rather than intuition.
• Stay hands-on with emerging architectures (transformers, diffusion models, agentic frameworks) and evaluate their fit for business problems.

Production Engineering & MLOps

• Build reproducible, version-controlled ML pipelines covering data ingestion, feature engineering, training, and deployment.
• Deploy models into production using containerized services (Docker/Kubernetes) and serving frameworks (FastAPI, TorchServe, Triton, or similar).
• Set up model monitoring for drift, latency, and accuracy degradation, and build automated retraining triggers where appropriate.
• Own cost and performance optimization for models running at scale, including GPU utilization and inference latency.

Cross-Functional Collaboration

• Work closely with data engineers to ensure clean, well-structured, and timely data pipelines feed your models.
• Partner with product managers to translate ambiguous business goals into measurable ML objectives and success metrics.
• Communicate model capabilities, limitations, and risk trade-offs clearly to non-technical stakeholders — including where a model might fail and why.
• Mentor junior team members on ML best practices, code quality, and experiment design.

Governance & Responsible AI

• Apply basic model governance practices: documentation, bias/fairness checks, and reproducibility standards.
• Ensure data privacy and security compliance when handling sensitive training data.

Desired Candidate Profile

• 2–6 years of hands-on experience building and deploying ML/AI models in a production environment (not just academic or Kaggle projects).
• Strong Python skills and fluency with at least one deep learning framework (PyTorch or TensorFlow).
• Practical experience with LLM tooling — LangChain, LlamaIndex, vector databases (Pinecone, Weaviate, FAISS), or similar — is highly valued.
• Solid grounding in statistics, linear algebra, and core ML algorithms; you should be able to explain why a model works, not just that it works.
• Experience with cloud ML platforms (AWS SageMaker, Azure ML, or GCP Vertex AI) and containerization.
• Comfortable working in ambiguous, fast-changing environments where the "right" approach isn't always obvious upfront.
• Bonus: contributions to open-source ML projects, published research, or a strong portfolio of deployed AI projects.

Growth Path: ML Engineer Senior ML Engineer Staff/Lead ML Engineer or ML Architect Head of AI/ML

Perks & Benefits: Health insurance, performance bonus, ESOPs (if startup), certification and conference budget, flexible/hybrid work, dedicated GPU/compute access for experimentation.

Salary variants by level:

• Fresher (0–1 yr): 6,00,000 – 14,00,000
• Senior (6–10 yrs): 35,00,000 – 70,00,000+