Senior AI/ML Engineer - Unifyed

Job Title: Senior AI/ML Engineer Experience Level: 6+ Years Employment Type: Full-Time Location: Gurugram, Sector 33 Shift Timings: 12:00 PM - 9:00 PM IST About the Role: We are looking for a hands-on Senior AI/ML Engineer who can own the full lifecycle of machine learning solutions – from problem definition and data modelling to training, deployment, monitoring, and continuous improvement.

You should be comfortable working with messy real-world data, designing robust data models & features, building and training models, and shipping them to production with proper MLOps practices. You must also be aware of the current AI/ML landscape (LLMs, embeddings, vector search, modern tooling) and know when to use what.

Key Responsibilities

End-to-End Solution Ownership Work with product / domain stakeholders to understand business problems and define ML use cases Translate requirements into data & model design, success metrics, and clear technical plans Own the full pipeline: data ingestion → cleaning → feature engineering → model training → evaluation → deployment → monitoring Data Modelling & Feature Engineering Design and maintain data models / schemas optimized for analytics and ML training (batch & real time) Perform exploratory data analysis (EDA) and feature engineering to improve signal quality and model performance Work closely with data engineering to ensure reliable, well-documented datasets Model Training & Evaluation Build, train, and tune models for tasks such as: prediction, classification, ranking, recommendations, anomaly detection, and NLP.

Use appropriate techniques (traditional ML, deep learning, embeddings, LLMs) based on the problem Define and track offline and online metrics; run A/B tests or controlled experiments where applicable MLOps & Productionization Build reproducible training pipelines (e.g., using MLflow, Airflow, Kubeflow, or similar tools) Package and deploy models as APIs / microservices or batch jobs, using containers and cloud services Implement monitoring, alerting, and logging for model performance, data drift, and system health Manage model versions, rollouts, and rollback strategies AI/ML Architecture & Best Practices Evaluate and integrate modern AI tools: vector databases, embedding models, LLM APIs, RAG architectures, etc.

Ensure solutions follow security, privacy, and compliance best practices (e.g., PII handling, access control) Write clear documentation for data flows, models, and services Mentor junior engineers/data scientists and contribute to engineering standards and guidelines Must-Have Skills & Experience Core Technical Skills (6+ Years) Python Programming: Strong expertise in ML libraries (pandas, numpy, scikit-learn, Py.

Torch, Tensor. Flow) SQL & Databases: Solid SQL skills and hands-on experience with relational and NoSQL data stores Production ML: Demonstrated experience shipping end-to-end ML projects to production (not just notebooks / POCs) ML Fundamentals: Deep understanding of supervised/unsupervised learning, evaluation metrics, overfitting, bias/variance, data leakage MLOps & DevOps Senior AI/ML Engineer Experiment tracking tools (MLflow, Weights & Biases) Model versioning and packaging (Docker, virtualenv, Conda) CI/CD pipelines for ML services Infrastructure as Code and containerization best practices Cloud & Architecture Proficiency with at least one major cloud platform: AWS: S3, EC2, Sage.

Maker, Lambda, RDS, DynamoDB GCP: Cloud Storage, Compute Engine, Vertex AI, Firestore Azure: Blob Storage, VMs, Azure ML, Cosmos DB API design (REST/GraphQL) and microservice architecture integration Understanding of scalability, latency, and cost optimization Modern AI/ML Landscape Awareness Exposure to LLMs & embeddings (OpenAI, Hugging.

Face, Anthropic, etc.) Familiarity with vector search & semantic search platforms (Open. Search, Elasticsearch, Pinecone, Weaviate, pgvector) Ability to make technical trade-offs between classical ML vs deep learning vs LLM-based approaches Understanding of cost, latency, and accuracy considerations for each approach Soft Skills Problem-Solving Strong analytical thinking with ability to question requirements and propose better solutions Independence: Can drive projects from ideation through production deployment with minimal guidance Communication: Excellent at explaining technical trade-offs and complex concepts to both technical and non-technical stakeholders Collaboration: Works well with cross-functional teams (product, data engineering, infrastructure, security