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.