Senior AI/ML Engineer - Unifyed

ElevenX Capital; Gurgaon; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

A leading financial technology firm in Gurgaon is seeking a Senior AI/ML Engineer to take charge of developing and implementing machine learning solutions. The successful candidate will handle everything from problem definition to deployment and monitoring. Strong expertise in Python and SQL, alongside experience in production ML, is crucial. The position offers the opportunity to work with innovative technologies in a collaborative environment.

Qualifications

• 6+ years of experience with strong Python expertise in ML libraries.
• Solid SQL skills and experience with both relational and NoSQL databases.
• Proven track record of shipping end-to-end ML projects to production.
• Deep understanding of machine learning principles and concepts.

Responsibilities

• Own the full lifecycle of machine learning solutions from problem definition to deployment.
• Define ML use cases and translate requirements into data and model design.
• Build, train, and tune ML models for various tasks.
• Implement monitoring and model versioning.

Skills

Python Programming

SQL & Databases

Production ML

ML Fundamentals

Model versioning and packaging

Experiment tracking tools

CI/CD pipelines

Understanding of scalability

Tools

MLflow

PyTorch

TensorFlow

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 modeling 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 usecases
• 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 & 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
• 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

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)