Top Tech Jobs & Startup Jobs

Build and deploy production-grade generative AI and agentic systems for enterprise customers. Responsibilities include developing RAG applications, backend services, data pipelines, evaluation harnesses, monitoring, guardrails, and cloud-native AWS infrastructure. The role owns model and agent reliability, cost, latency, drift detection, and release quality. It also involves architectural decisions, customer handover, documentation, mentoring engineers, and collaboration with forward-deployed executives and engineers.

Build and deploy production generative AI systems, including LLM applications, agentic workflows, retrieval pipelines, and evaluation harnesses. Work directly inside customer operations to understand domains, redesign business processes, and ship maintainable cloud-native solutions on AWS. Own adoption and measurable business outcomes while collaborating with executives, operators, and engineering teams. Contribute to reusable industry blueprints and remain hands-on across backend, data, and AI development.

Build and deploy production generative AI, RAG, and agentic systems for enterprise clients. Translate business workflows into technical solutions, develop evaluation harnesses, write production code, and deliver cloud-native systems on AWS. Lead architecture reviews, technical proposals, scoping, monitoring, cost optimization, and client handoff. Partner with senior stakeholders, own technical direction, mentor engineers, and help evolve reusable AI Blueprints across financial services, insurance, healthcare, and life sciences.

Design and deliver cloud-native AI and LLM-based solutions from discovery through production. Lead presales, architecture, RAG and agentic implementations, deploy and operate ML/LLM systems, write production-grade Python, build ETL/ELT pipelines, implement MLOps/LLMOps/AgentOps, mentor engineers, and manage client technical relationships.

Designs, builds, and deploys production-grade Python AI systems, including RAG and agentic workflows, for enterprise clients. Responsibilities include architecture ownership, backend and API development, AWS deployment, system design decisions, client communication, presales support, technical documentation, mentoring, code reviews, testing, and production AI/ML model maintenance. The role requires embedded client delivery, direct stakeholder engagement, and end-to-end ownership from discovery through implementation.

Embed with customers to learn and redesign critical workflows, then build and ship production GenAI/LLM systems on AWS. Own evaluation, deployment, and operational reliability, collaborate with senior stakeholders, and drive measurable business outcomes using Provectus blueprints and agentic tooling.

Embed with client operators, learn and rebuild critical business processes, and deliver production GenAI/LLM systems on AWS. Own design, shipping, evaluation, and operational handoff while working cross-functionally with senior stakeholders to move business metrics.

Design, build, and improve ML models and production pipelines; collaborate with engineers, data scientists, and PMs; develop experimentation roadmaps; set up reproducible environments; monitor and maintain production models; document models and processes; propose and apply new AI/ML solutions.

Own partner-sourced and self-sourced enterprise sales motions: convert Anthropic/AWS partner leads to signed SOWs, prospect new logos in Financial Services and Life Sciences, run full sales cycles, maintain joint pipelines and co-sell activities, and coordinate with delivery and architecture teams to scope consulting engagements.

The Senior ML Engineer will create and improve ML models, collaborate with teams, monitor models in production, and maintain documentation.