Integrate and instrument LangChain for composable chains, agents and tooling; use Langfuse (or equivalent tracing) to capture prompts, model calls, RAG traces……
Knowledge of Generative AI, LLMs, AI agents, and agentic frameworks. Integrate AI agents with governed data products managed through Unity Catalog.…
Experienced in prompt engineering, context optimization, and AI pipeline design. Background in automation systems, workflow optimization, or enterprise AI tools……
· Collaborate with the team to design, implement, and test features that interact with LLMs.*. · Knowledge of generative AI tools and methodologies.*.…
As part of this commitment, the use of artificial intelligence (AI) or other tools to assist with responses during interviews (whether in-person or virtual) is……
Strong understanding of distributed systems, async processing, event-driven architectures, and scalable backend design. 401k with 3% company match.…
Communicate technical AI concepts clearly to both technical and non-technical audiences. Engineering as a Service provides complete design, implementation, and……
AI-assisted development: Mid-level or stronger hands-on use of AI coding tools and agentic workflows (GitHub Copilot, Copilot/Claude agents, prompt engineering,……
Bachelor’s degree in computer science or related field. Experience building agentic AI solutions using LangChain/LangGraph and integrating tools via MCP (Model……
Bachelor’s degree in computer science or related field. Collaborative solution delivery: partner with business stakeholders to gather requirements, leverage……
Present technical and business concepts to both technical and executive audiences. Strong understanding of prompt engineering and AI application design.…
AI-assisted development: Mid-level or stronger hands-on use of AI coding tools and agentic workflows (GitHub Copilot, Copilot/Claude agents, prompt engineering,……
Bachelor's degree in computer science, engineering, or related field or equivalent experience, bootcamp training, or demonstrable project work.…
Communication: Excellent communication and interpersonal skills, with the ability to effectively collaborate with cross-functional teams and present technical……
Embed Generative AI tools and frameworks in current tools for personas like Research, Banking , Sales and Trading. O Agentic framework / Agent harness.…
As a Lead Software Engineer on our AI Platform Team, you will drive the design and development of the next generation of agentic, reliable, and scalable GenAI……
You will own the Developer Relations charter for AI-native engineering, shaping how developers leverage coding assistants, autonomous agents, AI-assisted……
Integrate AI agents with enterprise systems via REST APIs, databases, and cloud services. Collaborate with product, cloud, and application teams to embed AI……
You've been building agents long enough to have opinions — about context engineering, tool design, when to use a skill vs. a tool, what evals catch and miss,……
This individual will play a key role in architecting and developing scalable AI platforms, mentoring engineers, and driving technical innovation across the……
Develop the Python control-plane agents that watch pods, report run state to the platform, and keep clusters in sync. REST and tRPC API design.…
Lead technical design discussions and help set architectural direction. Our frontend is built with Typescript and React, using a combination of Apollo GraphQL……
Bachelor’s or master’s degree in Computer Science or a related field. Working knowledge of UML and technical documentation practices to communicate system……
Integrate agents with enterprise platforms and APIs. Drive best practices in prompt engineering and context engineering. Multi-agent system design patterns.…
Demonstrated experience designing, developing, and deploying AI agents that interact with APIs, databases, enterprise systems, and external tools.…
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CVS Scottsdael, AZ **HYBRID FROM DAY 1 About the Role We are seeking an experienced AIML Engineer to design, build, and operate AI/ML infrastructure and agentic systems. This role involves developing MCP servers and agents, integrating LLMs, and implementing RAG pipelines for production environments. Key Responsibilities · Design, build and operate MCP servers and MCP agents that host, orchestrate and monitor AI/agent workloads. · Develop agentic AI, prompt engineering patterns, LLM integrations and developer tooling for production use. · Own deployment, scaling, reliability and cost-efficiency on Kubernetes/Docker and Google Cloud with automated CI/CD · Design and implement RAG (Retrieval Augmented Generation) pipelines and integrations with vector stores and retrieval tooling; use LangChain and Langfuse for orchestration, chaining, and observability. Core Responsibilities · Implement and maintain MCP server and agent code, APIs, and SDKs for model access and agent orchestration. · Design agent behavior, workflows and safety guards for agentic AI systems. · Create, test and iterate prompt templates, evaluation harnesses and grounding/chain of thought strategies. · Integrate LLMs and model providers (self hosted and cloud APIs) with unified adapters and telemetry. · Build developer tooling: CLI, local runner, simulators, and debugging tools for agents and prompts. · Containerize services (Docker), manage orchestration (Kubernetes/GKE), and optimize nodes, autoscaling and resource requests. · Ensure observability: logging, metrics, traces, dashboards, alerting and SLOs for model infra and agents. · Create runbooks, playbooks and incident response procedures; reduce MTTR and perform postmortems. · Design and maintain RAG workflows: document chunking, embeddings, vector indexing, retrieval strategies, re ranking and context injection. · Integrate and instrument LangChain for composable chains, agents and tooling; use Langfuse (or equivalent tracing) to capture prompts, model calls, RAG traces and evaluation telemetry. Required Skills & Experience · 5+ years of Strong Software Engineering (Python/NodeJS), system design and production service experience. · 2+ years of Experience with LLMs, prompt engineering, and agent frameworks. · 2+ years of Experience Practical experience implementing RAG: embeddings, vector DBs and retrieval tuning. · 2+ years of Experience with LangChain patterns and with toolchain telemetry (Langfuse or similar) for prompt/model traceability. · 5+ years of Experience with Kubernetes, Docker, CI/CD and infrastructure as code experience. · 2+ years of Experience with Practical experience with Google Cloud Platform services · 2+ years of Experience with Observability, testing, and security best practices for distributed systems. · 2+ years of Experience with evaluating and mitigating retrieval/augmentation failures, hallucinations, and leakage risks in RAG systems. · Familiarity with vendor and open source vector stores and embedding providers. · Familiarity with CI/CD pipelines (Jenkins, GitHub Actions, GitLab CI, or ArgoCD).