Senior Forward Deployed Engineer

Lead architecture and design of AI/ML solutions tailored to unique client requirements, balancing scalability, performance, and maintainability.

Guide clients through decisions related to LLM integration, data engineering, infrastructure design, and model operations.

Own end-to-end solution delivery, from exploration and rapid prototyping to production deployment and handoff.

Serve as the senior technical point of contact during client engagements—trusted by executives, product leaders, and technical teams alike.

Conduct technical discovery, shape implementation roadmaps, and communicate risks and trade-offs clearly to diverse stakeholders.

Influence long-term client strategy through technical advisory, architectural reviews, and enablement sessions.

Build AI-driven applications using modern frameworks and tools (e.g., Python, LangChain, FastAPI, Docker, cloud-native platforms).

Design and implement workflows involving generative AI (e.g., retrieval-augmented generation, prompt tuning, embeddings pipelines).

Collaborate with internal research, data, and platform teams to extend company-wide best practices to client-specific contexts.

Mentor junior engineers and consultants across engagements, fostering a high bar for technical quality and client service.

Help define internal tooling, architecture patterns, and deployment playbooks based on field experience.

Represent the engineering team in pre-sales discussions, project scoping, and high-stakes strategic planning.

5–8+ years of software engineering experience, with at least 3+ years in AI/ML-focused roles.

Demonstrated experience leading technical delivery in a client-facing or consulting environment.

Strong understanding of modern AI architectures, including experience deploying LLMs, building RAG pipelines, and working with vector databases (e.g., Pinecone, Weaviate, FAISS).

Fluency in Python, with experience in cloud platforms (AWS, GCP, or Azure), containerization, and scalable API design.

Excellent interpersonal and communication skills; able to command technical and business conversations with senior stakeholders.

Experience in regulated or high-complexity environments (e.g., finance, healthcare, legal, government).

Familiarity with enterprise AI lifecycle tools (e.g., MLflow, Ray, Databricks, Airflow).

Contributions to open-source AI tools or internal frameworks in previous roles.

Willingness to travel for key client engagements (if applicable).