AI Engineer
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Job Description
AI Engineer focused on building and operationalizing AI and LLM-based applications at Veritas Automata. Collaborating with cross-functional teams to deliver scalable and secure AI solutions.
Responsibilities:
- Design and implement AI/LLM‑powered capabilities including agent workflows, tool‑use actions, retrieval‑based systems, and structured output pipelines.
- Build integrations with major model providers (OpenAI, Azure OpenAI, Anthropic) and open‑source model ecosystems.
- Develop and optimize RAG pipelines, embeddings, vector search, and semantic retrieval patterns.
- Implement evaluation harnesses, guardrails, prompt management, and safety validation workflows.
- Collaborate with backend, frontend, and data engineers to deliver scalable AI‑driven features.
- Integrate AI capabilities into Kubernetes‑based microservices environments using modern APIs and deployment patterns.
- Configure and operate model‑serving environments (vLLM, TGI, KServe) including tuning for latency, throughput, and cost.
- Implement observability for AI systems including telemetry, metrics, traces, structured logs, and prompt evaluations.
- Support CI/CD automation, model versioning, feature flagging, and safe rollout of AI functionality.
- Contribute to documentation, architectural diagrams, and reusable internal AI patterns.
- Mentor junior engineers and support skill development across AI engineering best practices.
Requirements:
- 5–8 years of experience in software engineering, AI engineering, ML engineering, or distributed systems engineering.
- Hands‑on experience building AI/LLM applications including retrieval, embeddings, structured outputs, and function/tool calling.
- Strong proficiency in Python and TypeScript/JavaScript, including API development and workflow orchestration.
- Familiarity with agent frameworks (LangChain, LlamaIndex, DSPy, Semantic Kernel) and evaluation patterns.
- Experience with vector databases (FAISS, Milvus, Pinecone, Chroma) and semantic search pipelines.
- Working knowledge of Kubernetes, containers, Git‑based workflows, CI/CD, and cloud‑native deployment patterns.
- Strong understanding of distributed system design, performance tuning, and observability.
- Bachelor's degree in Computer Science, Engineering, Mathematics, or equivalent practical experience.
- Advanced English level (written and spoken) to communicate effectively across global teams.
Benefits:
- Work-life integration: We support work-life balance and create greater synergy among work, home, family, and personal well-being.
- Prolonged periods of sitting at a desk and working on a computer.
- Occasional travel to the client’s site may be required.
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