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 \\u2013 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, PyTorch,\ TensorFlow)

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, SageMaker, 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, HuggingFace, Anthropic, etc.)\ Familiarity with vector search & semantic search platforms (OpenSearch, 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

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Senior AI/ML Engineer - Unifyed

Job Description

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