Description

The senior engineer will own the AI roadmap, act as the primary technical subject‑matter expert, and collaborate closely with IT, security, and business teams to operationalize AI across the organization, shaping how AI is adopted and integrated into enterprise workflows.

• Design, build, and maintain production‑grade agentic AI systems using frameworks like OpenAI, LangChain, LlamaIndex, AutoGen, or CrewAI.
• Architect conversational AI assistants and multi‑channel agents for enterprise‑ready user experiences.
• Integrate large language models with APIs, databases, vector stores, and enterprise systems to create end‑to‑end AI workflows.
• Implement memory, retrieval, planning, and reasoning components for goal‑oriented, context‑aware agents.
• Apply advanced prompting techniques (prompt engineering, chain‑of‑thought, tool use) to improve accuracy, latency, and cost efficiency.
• Lead development of Retrieval‑Augmented Generation (RAG) architectures ensuring factual accuracy and traceability across knowledge sources.
• Establish evaluation frameworks with metrics, hallucination detection, regression testing, and human‑in‑the‑loop feedback.
• Operationalize AI in production with monitoring, logging, observability, cost controls, and continuous improvement loops.
• Build LLMOps/MLOps pipelines covering model versioning, experimentation, deployment, rollback, and environment promotion.
• Ensure security, privacy, and ethical AI compliance through data protection, prompt/output safeguards, and policy enforcement.
• Collaborate with product managers and stakeholders to translate business objectives into measurable AI solutions.
• Conduct feasibility assessments and develop proofs‑of‑concept for emerging AI tools before production adoption.
• Mentor junior engineers and educate non‑technical stakeholders on AI capabilities, limitations, and responsible use.
• Collaborate effectively with managers, cross‑functional teams, and teammates to achieve shared goals.
• Apply an analytical approach to tackle complex problems, identify root causes, and develop data‑driven solutions.

Requirements

Success in this role requires curiosity, a product‑focused mindset, strong collaboration across managers and global teams, persistence, ownership, and an analytical approach to solving complex problems with data‑driven solutions.

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field, or equivalent practical experience.
• 5+ years of software engineering experience with strong Python skills and familiarity with JavaScript/TypeScript.
• 2+ years of hands‑on experience building solutions using agentic AI and LLM frameworks such as OpenAI, Hugging Face, LangChain, LlamaIndex, AutoGen, or CrewAI.
• Experience with cloud platforms (AWS, GCP, Azure) and modern deployment practices including Docker and Kubernetes.
• Proficiency with vector databases and embedding systems like Pinecone, FAISS, Weaviate, Chroma, or Milvus.
• Proven track record delivering generative AI applications in production environments.
• Solid background in NLP, model training, fine‑tuning, and evaluation techniques.
• Knowledge of LLM performance considerations, latency, cost optimization, and prompt reliability.
• Awareness of AI security, data privacy, and responsible AI principles in enterprise settings.
• Strong communication skills and ability to influence both technical and non‑technical stakeholders across global time zones.
• Demonstrated ability to drive technical decisions, mentor peers, and contribute to a high‑performing engineering culture.
• Cultivate strong curiosity and a product‑focused mindset.

Benefits

Training + Development

Interview process

Visa Sponsorship

Security clearance