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

📌 Senior AI/ML Engineer - Unifyed (Gurugram)
🏢 ElevenX Capital
📍 Gurugram