Senior AI/ML Engineer

Unifyed; Gurgaon; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

A technology solutions company in Gurgaon is seeking a Senior AI/ML Engineer to own the end-to-end lifecycle of machine-learning solutions. You will work closely with stakeholders to define ML use cases, build robust data models, and ensure the deployment of high-performance ML applications. The ideal candidate should have over 6 years of expertise in Python and ML libraries, experience in delivering production-level ML projects, and a solid understanding of various ML methodologies. This role promises significant challenges in a dynamic environment.

Qualifications

• 6+ years of experience in Python programming and machine learning libraries.
• Experience in shipping end-to-end ML projects to production.
• Deep understanding of ML fundamentals including supervised and unsupervised learning.

Responsibilities

• Own the full lifecycle of machine-learning solutions from problem definition to deployment.
• Work with stakeholders to understand business problems and define ML use cases.
• Build, train, and tune models for prediction, classification, and recommendation tasks.

Skills

Python Programming

SQL & Databases

Production ML

Understanding of ML Fundamentals

Experiment tracking tools

Docker & CI/CD pipelines

API design

Tools

pandas

numpy

scikit-learn

PyTorch

TensorFlow

MLflow

Docker

AWS

GCP

Azure

Job description

Gurgaon Kty., India | Posted on 12/10/2025

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
• Engineering design and maintain data models / schemas optimized for analytics and ML training (batch & realtime)
• 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
• 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
• 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
• 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

• 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
• Proficiency with major cloud platforms (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

Soft Skills

• 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)

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.