AI Engineer
Skills
Agentic AI DevelopmentFine TuningGenerative AILarge Language ModelsPythonRetrieval-Augmented GenerationVector Databases
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- 10+ years of engineering experience with at least 2 years in customer-facing field engineering solutions engineering or forward deployed engineering roles
- Proven experience building and deploying AI ML data-intensive or enterprise-grade applications in production
- Strong full-stack development experience with Python Node.js or Go and React or Vue
- DevOps experience with Docker Kubernetes CI/CD and modern cloud-based deployment practices
- Experience designing and implementing enterprise data pipelines and system integrations
- Strong knowledge of REST APIs Python SQL GraphQL Webhooks and enterprise integration patterns
- Solid understanding of LLMs prompt engineering prompt tuning vector databases RAG pipelines and agentic workflows
- Experience with vector databases such as AstraDB Pinecone or Weaviate
- Familiarity with RAG frameworks such as LlamaIndex or Haystack
- Experience with agent and workflow orchestration tools such as LangChain LangGraph or CrewAI
- Ability to lead technical solution design and implementation for strategic enterprise customers
- Experience customizing platform components integrating APIs building reusable tooling or extending platform logic
- Strong understanding of observability monitoring versioning telemetry and trustworthy AI deployment practices
- Ability to translate ambiguous customer needs into clear actionable engineering plans
- Strong project ownership mentoring communication and collaboration skills across technical and business stakeholders
- Undergraduate degree master s degree or PhD in Computer Science Data Science or a related technical field
Nice to have
- Customer-focused
- Cross-functional collaborator
- Solution-oriented
- Strong communicator
- Technical leader
Day to day
- Lead architecture, prototyping, implementation, and post-deployment optimization of enterprise Agentic AI and Knowledge AI solutions
- Work closely with customers and internal teams to translate ambiguous business needs into scalable engineering plans and successful deployments
- Build customer-specific applications across data pipelines, multi-agent orchestration, RAG workflows, SLM fine-tuning, platform customization, and production-grade AI systems