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, 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