AI/ML Engineer - Engineering Excellence (Full Stack Developer)

Responsibilities

• Accountable for executing and driving mid-size feature application design and development efforts to completion, serving as a development lead on medium-scale projects and supporting the execution of larger efforts.
• Proficient at operating with considerable autonomy and discretion as you will significantly influence the way an application is designed and developed by providing subject specific expertise and an advanced level of understanding of application programming principles.
• Manage one or more Applications Development teams in an effort to accomplish established goals as well as conduct personnel duties for team (e.g. performance evaluations, hiring and disciplinary actions).
• Utilize in-depth knowledge and skills across multiple Applications Development areas to provide technical oversight across systems and applications.
• Review and analyze proposed technical solutions for projects.
• Lead the design, development, and implementation of sophisticated AI/ML, Generative AI, and Agentic AI solutions, including AI assistant tools, in close collaboration with AI architects, product owners, and cross-functional engineering teams within the Engineering Excellence framework.
• Architect and develop end-to-end AI systems, leveraging both Python for machine learning workflows and Java for scalable enterprise-grade backend services, ensuring seamless integration with existing and new applications.
• Design and implement intelligent agentic systems that exhibit autonomous decision-making capabilities, enabling advanced automation and self-optimizing processes.
• Develop, fine-tune, and optimize Large Language Models (LLMs) using both parameter-efficient techniques and full fine-tuning, focusing on their integration into robust, production-ready systems supported by both Python and Java components.
• Drive the implementation and experimentation with advanced generative AI methods such as prompt engineering and Retrieval-Augmented Generation (RAG), ensuring their effective deployment and performance in enterprise environments.
• Own the deployment pipeline for AI models and associated services, leveraging CI/CD methodologies, containerization (Docker), and orchestration (Kubernetes) on leading cloud platforms (AWS, Azure, GCP) to ensure secure, scalable, and automated releases.
• Apply deep knowledge of database technologies (SQL and NoSQL) to design efficient data storage, retrieval, and management strategies for AI applications, ensuring data integrity and performance.
• Contribute to the establishment and enforcement of engineering best practices, architectural patterns, and tooling standards for full-stack AI development, advocating for maintainability, observability, and cost-efficiency.
• Stay abreast of the latest advancements in AI/ML, Agentic AI, cloud technologies, and DevOps trends, proactively sharing knowledge and driving adoption of innovative solutions.
• Ensure strict adherence to ethical AI guidelines, data privacy regulations, and compliance standards throughout the entire AI solution lifecycle.

Requirements

• Polyglot Programming Expertise: Python: Expert proficiency in Python for AI/ML development, including data manipulation (Pandas), scientific computing (NumPy), and machine learning frameworks.
• Java: Strong proficiency in Java (e.g., Spring Boot, Microservices, Enterprise Integration Patterns, RESTful APIs) for building scalable, high-performance, and resilient enterprise applications.
• AI/ML & Agentic AI: Experience with Generative AI, Agentic AI principles, and the development of AI assistant tools.
• Hands-on experience with LLMs and fine-tuning methods (e.g., LoRA, QLoRA, Adapter/Prefix Tuning, instruction tuning).
• Practical knowledge of model optimization techniques (e.g., compression, quantization) and familiarity with tools such as DeepSpeed, vLLM, GPTQ, or similar.
• Proficient in prompt engineering, prompt design tools/frameworks, and building robust RAG systems (hybrid search, multi-vector retrieval).
• Proficient with machine learning frameworks (PyTorch, TensorFlow, Keras) and distributed training.
• Strong skills in Natural Language Processing (NLP) techniques (NER, Dependency Parsing, Text Classification, Topic Modeling), transfer learning, and advanced learning paradigms.
• Familiarity with generative AI tools and libraries like LangChain, LlamaIndex, Hugging Face, and major GenAI APIs (e.g., OpenAI, Gemini, Claude, AWS Bedrock).
• Solid experience with both SQL (e.g., PostgreSQL, Oracle, MySQL) and NoSQL (e.g., MongoDB, Cassandra, DynamoDB) databases, including schema design, query optimization, and integration with applications.
• Extensive hands-on experience with at least one major cloud provider (AWS, Azure, or GCP), including services for compute, storage, networking, AI/ML, and data.
• Strong understanding and practical experience with CI/CD pipelines, automated testing, infrastructure as code (IaC), and configuration management.
• Expertise with containerization (Docker) and orchestration (Kubernetes, OpenShift).
• Familiarity with monitoring, logging, and alerting tools for production systems.
• Solid understanding of AI compliance, guardrails, Responsible AI practices, and enterprise security standards within a highly regulated environment.