Associate Engineer - AI / ML T6

About Albertsons Companies Inc.:As a leading food and drug retailer in the United States, Albertsons Companies, Inc. operates over 2,200 stores across 35 states and the District of Columbia. Our well-known banners across the United States, including Albertsons, Safeway, Vons, Jewel-Osco and others, serve more than 36 million U.S customers each week.We build and shape technology solutions that solve customers' problems every day, making things easier for them when they shop with us online or in a store. We have made bold, strategic moves to migrate and modernize our core foundational capabilities, positioning ourselves as the first fully cloud-based grocery tech company in the industry.Our success is built on a one-team approach, driven by the desire to understand and enhance the customer experience.

By constantly pushing the boundaries of retail, we are transforming shopping into an experience that is easy, efficient, fun and engaging.

About Albertsons Companies India:At Albertsons Companies India, we're not just pushing the boundaries of technology and retail innovation, we're cultivating a space where ideas flourish and careers thrive. Our workplace in India is a vital extension of the Albertsons Companies Inc. workforce and important to the next phase in the company's technology journey to support millions of customers' lives every day.At the Albertsons Companies India, we are raising the bar to grow across Technology & Engineering, AI, Digital and other company functions, and transform a 165-year-old American retailer. At Albertsons Companies India associates collaborate directly with international teams, enhancing decision-making processes and organizational agility through exciting and pivotal projects.

Your work will make history and help millions of lives each day come together around the joys of food and inspire their well-being.Contribute to build and integrate AI-powered applications leveraging Large Language Models, RAG pipelines, and modern AI engineering frameworks. & Contribute to engineering excellence through clean code practices, AI-assisted development, and continuous learning.

Key Responsibilities:Design and develop LLM-based applications such as chatbots, document Q&A systems, and AI assistants.Implement Retrieval-Augmented Generation (RAG) pipelines including chunking, embedding generation, vector storage, and response grounding.Integrate LLM APIs with function calling, streaming, and structured output handling into backend services.Build and expose RESTful APIs using FastAPI; contribute to containerized deployments using Docker/Podman.Leverage AI-assisted development tools (e.g., GitHub Copilot, Cursor, Claude Code) as a core part of the engineering workflow.Participate in code reviews, maintain clean and modular codebases, and adhere to engineering best practices.Stay current with advancements in Generative AI, LLMs, and the broader AI engineering ecosystem.

Required Qualifications:AI & LLM Skills:Understanding of Large Language Model fundamentals - architecture, context management, and inference parametersExperience with prompt engineering techniques including chain-of-thought, few-shot prompting, and system prompt designFamiliarity with Retrieval-Augmented Generation (RAG) - retrieval strategies, chunking, and response groundingExposure to agentic AI patterns - tool use, memory management, and multi-agent orchestrationAwareness of LLM limitations such as hallucinations, prompt injection, and context degradationProficiency in vibe coding tools (e.g., GitHub Copilot, Cursor, Claude code etc).

Programming & Technical Skills:Strong proficiency in Python - including asynchronous programming, modular design, and clean code practicesHands-on experience with LLM application frameworks such as LangChain, LangGraph, or LlamaIndexFamiliarity with LLM API integration - function calling, streaming, and structured output handlingAbility to build and expose backend services using FastAPI; understanding of containerization (Docker/Podman)Proficient with Git, Linux environments, and foundational cloud services (GCP / AWS / Azure)

Good to Have:Solid grounding in core ML concepts - supervised and unsupervised learning, model evaluation metrics, regularization, and optimizationHands-on exposure to deep learning using PyTorch or TensorFlow; conceptual understanding of LLM fine-tuning methodologiesFamiliarity of Natural Language Processing - tokenization, word and sentence embeddings, and semantic similarity measuresStatistical Reasoning - probability, distributions, hypothesis testing, and the ability to interpret and reason over insights and data meaningfully