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Senior AI / ML Engineer

Seven N HalfIndia

11 hours ago

Job description

Basic Information

Role Title : Senior AI / ML Engineer - Required Technical Skillset : Python, Java / Scala / Golang, ML frameworks at least one (TensorFlow / PyTorch), MLOps, Microservices, Cloud at least one (Azure / AWS / GCP), LLM / Generative AI - Work Location : Gurugram - Work Experience : 5-7 years

About the Team and Role

The AI Engineering and Solutions team at the Super-app is responsible for building and deploying next-generation AL Platform and intelligence data solutions across various consumer products and services on the Super-app platform. We engage closely with cross-functional teams—Product, Engineering, Marketing, Data Science, Customer Experience, and more—to drive data-informed strategies and deliver cutting-edge ML Platform advance data capabilities including Generative AI and LLM (Large Language Model) solutions for customer-facing applications.

We are currently seeking a Senior AI / ML Engineer with 5-7 years of experience. The ideal candidate will excel in designing and implementing scalable ML pipelines, integrating MLOps best practices, architecting microservices for low-latency model serving, and leveraging LLM-based techniques (including fine-tuning and agent development) to build advanced AI solutions. You will be instrumental in building a full-stack ML platform that supports a variety of high-impact AI initiatives.

Key Responsibilities

ML Platform Development

Architect and develop end-to-end ML pipelines for data ingestion, model training, and deployment at scale. - Implement scalable and secure infrastructure on Azure / AWS / GCP using IaC (e.g., Terraform, CloudFormation).

Model Engineering & MLOps

Collaborate with data scientists to productionize ML / DL models, including Generative AI / LLMs. - Set up and manage automated CI / CD pipelines for model versioning, testing, deployment, and monitoring (e.g., MLflow, Kubeflow). - Fine-tune large language models on domain-specific data and optimize for real-world scenarios (e.g., prompt engineering, RAG, agent-based architectures).

Microservices & API

Develop high-performance microservices in Java / Scala / Golang or Python for real-time model inference. - Containerize services using Docker and orchestrate them via Kubernetes for scaling and reliability. - Integrate APIs that deliver low-latency, customer-facing AI features (e.g., chatbots, generative text / image solutions).

Performance & Scalability

Optimize ML systems for low-latency applications and high-throughput services, particularly for LLM-based endpoints. - Implement caching, load balancing, and auto-scaling strategies to handle large-scale traffic.

AI / LLM Innovation

Stay current with state-of-the-art LLM, Generative AI, and agent-based technologies to drive continuous innovation. - Experiment with advanced techniques (e.g., reinforcement learning from human feedback, multi-modal embeddings) to enhance platform capabilities. - Customize GPT / LLM for specific use cases using Continued Pre-Training (CPT) or Instruction Fine Tuning (IFT).

Product & Business Enablement

Work with product managers and business stakeholders to shape the AI / ML roadmap, ensuring alignment with strategic objectives. - Translate complex technical solutions into clear business value propositions and drive ROI analysis for ML initiatives.

Collaboration & Cross-Functional Engagement

Partner with engineering, data, and business teams to integrate AI / ML solutions seamlessly into the Tata Neu ecosystem. - Provide technical mentorship, best practices, and leadership to junior engineers and data scientists.

Competencies for the Role

Educational Background

B.Tech / BE / M.Tech or equivalent in Computer Science, Data Science, or a related field.

Technical Expertise

Programming : Expert in Python and at least one of Java / Scala / Golang for building robust microservices. - ML & LLM Frameworks : Hands-on experience with TensorFlow, PyTorch, or Scikit-learn; familiarity with LLM fine-tuning and associated libraries (e.g., Hugging Face). - Cloud & DevOps : Proficiency in cloud platforms (Azure / AWS / GCP) and container orchestration (Kubernetes, Docker). - Data Processing : Familiarity with Spark / Kafka or similar technologies for large-scale or real-time data workflows. - Generative AI, Agents and RAG :

Experience of building applications with LangChain, LangGraph, LlamaIndex and similar frameworks and libraries.

Exposure to Vector Databases, Embedding Models, and Semantic Similarity Search. - Expertise in advanced Prompt Engineering and Retrieval Augmented Generation (RAG) techniques for working with structured and unstructured data. - Experience in applying AI to practical and comprehensive technology solutions, and developing and deploying machine learning systems into production.

MLOps & Automation

Experience in setting up CI / CD pipelines, model registries, and feature stores. - Knowledge of best practices for model monitoring, logging, and drift detection.

Analytical & Problem-Solving Skills

Ability to design experiments, interpret complex data, and create actionable insights. - Familiarity with advanced statistical and ML techniques, including advanced NLP, LLM fine-tuning, and agent-based AI.

Product & Business Acumen

Proven track record of delivering ML solutions that impact business metrics and user experience. - Capable of balancing technical trade-offs with product requirements and ROI considerations.

Communication & Leadership

Strong written and verbal communication skills for stakeholder alignment. - Demonstrated ability to lead projects and mentor cross-functional teams.

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