Sr. Agentic Service Engineer

Responsibilities

    You’ll play a pivotal role in defining modular agent architectures integrating powerful foundation models (LLMs and beyond)
    Architect platform solutions that deliver flexibility, resilience, and cost optimization while meeting stringent security and privacy requirements.     Collaborating with AI/ML teams, data scientists, and infrastructure organizations, you'll translate the unique requirements of agentic workloads into scalable platform capabilities that enable rapid innovation.     Lead multi-functional technical initiatives, establish platform engineering standards, and drive the strategic roadmap for AI agent infrastructure.     Drive the real-time orchestration of agents that interact with data, APIs, and users—unlocking smarter, faster, and more adaptive applications at scale.

Minimum Qualifications

    4+ years in ML engineering with experience in large-scale software system design and implementation.
    Strong understanding of generative AI models, particularly large language models (LLMs) with proven experience in building complex agentic systems using LLMs.     Strong Python programming skills, with a background in developing scalable and robust services using FastAPI or similar frameworks.     Experience in Machine Learning with a particular emphasis on Large Language Models (LLMs), Retrieval Augmented Generation(RAG) and Generative AI.

Preferred Qualifications

    Bachelor’s or Master’s Degree Computer Science, Artificial Intelligence, Machine Learning, or a related field, or related experience.
    Communicating effectively, both written and verbal, with technical and non-technical multi-functional teams.     Proven track record of building enterprise-grade ML pipelines (data prep, distributed training, optimization, monitoring) in cloud environments (AWS, GCP, Azure).     Extensive knowledge with popular LLMs such as Gemini, Claude, and GPT     Demonstrated experience with building AI agents, LLMs for tool use, and Multimodal-LLMs.     Experience using one or more of the following: Reinforcement learning, Distillation, RLHF fine-tuning, RAG/VectorDB, LLM uncertainty quantification.     Experience with ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn)     Understanding of AI memory systems, including contextual embeddings and long-term memory management; bonus for experience with LangMem or LangGraph     Experience working on platform engineering or a developer experience platform is a plus.     Hands-on experience with cloud services and containerization technologies