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.