Machine Learning Engineer

Designation: Senior ML Engineer

Experience: 5+ years Role: The Technical Architect – ML Engineering will have a clear understanding of building scalable, production-grade machine learning systems and AI-powered applications. The role involves understanding business problems, translating them into ML/AI solutions, and architecting/designing systems that are high-performing, secure, scalable, reproducible, and testable. It is a hands-on role involving building ML pipelines, fine-tuning models, deploying to cloud, and taking ownership of delivery by working closely with data scientists, ML engineers, and junior team members. Responsibilities include:

• Minimum 1–2 years in designing ML/AI solutions as a technical architect or lead ML engineer
• Overseeing the development, training, evaluation, and deployment of ML and GenAI systems
• Collaborating with different stakeholders, including data scientists, product teams, DevOps, and customers
• Providing technical leadership and mentorship to ML engineering and data science teams
• Defining ML system design standards, model governance practices, and MLOps best practices

Requirements: Passion for building and delivering great ML systems with a strong sense of ownership.

• Minimum 3 years of experience in software/ML engineering, with at least 2 years focused on machine learning, deep learning, or applied AI
• Strong experience in architecting and developing end-to-end ML pipelines — from data ingestion and feature engineering to model training, deployment, and monitoring
• Hands-on experience with LLM fine-tuning (LoRA, QLoRA, PEFT, RLHF, instruction tuning) and building RAG-based applications
• Experience designing and deploying multi-tenant ML/AI SaaS solutions
• Experience designing solutions that are highly scalable and cost-optimized for inference at scale
• Experience building secure ML applications including model security, data privacy, PII handling, and prompt-injection defenses
• Expertise in working with structured and unstructured data at scale, including SQL and vector databases (Pinecone, Weaviate, FAISS, pgvector, Milvus, etc.)
• Strong understanding of model evaluation, experiment tracking, drift detection, and continuous training

Technical Competencies:

• Programming languages – Python (primary), SQL; familiarity with one of Go/Java/TypeScript is a plus
• Data Science & ML – NumPy, Pandas, Scikit-learn, XGBoost/LightGBM, statistical modeling, feature engineering
• Deep Learning – PyTorch (preferred), TensorFlow, Hugging Face Transformers
• LLM & GenAI – LLM fine-tuning (LoRA/QLoRA/PEFT), RLHF/DPO, embeddings, RAG architectures, prompt engineering, evaluation frameworks (RAGAS, DeepEval, etc.)
• LLM Frameworks – LangChain, LangGraph, LlamaIndex; agentic workflows and multi-agent orchestration
• MCP (Model Context Protocol) – designing and integrating MCP servers/clients for tool-augmented LLM applications
• MLOps – MLflow, Kubeflow, Weights & Biases, DVC, Airflow/Prefect, model registries, CI/CD for ML, feature stores (Feast, Tecton)
• Model Serving & Inference – FastAPI, BentoML, Triton Inference Server, TorchServe, vLLM, TGI, Ray Serve
• Cloud (any one strong, familiarity with others) –
• Azure: Azure ML, Azure OpenAI, AKS, Azure Functions, ADF, Event Hub, Cognitive Services
• AWS: SageMaker, Bedrock, Lambda, EKS, Step Functions, Kinesis
• GCP: Vertex AI, GKE, Cloud Functions, Dataflow, Pub/Sub
• Containerization & Orchestration – Docker, Kubernetes, Helm
• Observability for ML – LangSmith, Langfuse, Arize, WhyLabs, Evidently, Prometheus/Grafana
• Testing – PyTest, model unit testing, data validation (Great Expectations, Pandera)

Functional Competencies:

• Must have very good problem-solving skills, especially in ambiguous, data-driven contexts
• Must have excellent design, coding, and refactoring skills with focus on reproducibility
• Must have very good communication and presentation skills, including ability to explain ML concepts to non-technical stakeholders
• Should be a lateral thinker who provides simple, innovative solutions to complex ML/AI problems
• Should be able to participate in multiple projects simultaneously
• Must have experience deploying ML/LLM systems in production at scale
• Familiarity with continuous integration and deployment practices (CI/CD) and their ML extensions (CT — Continuous Training, CM — Continuous Monitoring)
• Awareness of Responsible AI practices — bias, fairness, explainability (SHAP, LIME), and AI governance

Qualification: Diploma, B.E. / B.Tech / B.C.S. / M.E. / M.Tech / M.C.A / M.C.M. Specialization in Computer Science, AI/ML, Data Science, or Statistics preferred.