Senior AI/ML Engineer - Unifyed at EXC Managed… · Gurgaon

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 & ExperienceCore 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 SkillsProblem-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