Senior AI/ML Engineer - Agentic AI
Tasks
- Add tracing and span capture
- Architect multi-agent systems
- Automate scoring
- Build offline evaluation harnesses
- Build online evaluation harnesses
- Containerize services
- Define evaluation strategy
- Design orchestration patterns
- Design tool integration architecture
- Drive prototype to production
- Implement Human in the Loop
- Implement guardrails
- Implement instrumentation and observability
- Implement memory and planning
- Implement trace eval fix loop
- Integrate tools via MCP
- Lead multi-agent systems
- Measure tool call accuracy
- Measure trajectory quality
- Mentor engineers through code reviews
- Optimize cost and latency
- Optimize failure modes
- Perform architecture reviews
- Run regression testing
- Set CI CD evaluation gates
- Set engineering standards
- Set up scalable serving
- Write design documentation
Perks/Benefits
- N/A
Skills/Tech-stack
API Design | AWQ | Agent systems | Annotation Workflows | Application development | Arize | Asynchronous programming | Autogen | Automated scoring | CI/CD | Context Management | CrewAI | Deep learning | Fine Tuning | GPTQ | Human-in-the-loop | INT4 Quantization | Inference Optimization | Int8 Quantization | LLM Application Development | LLM application | LLM-as-a-Judge | Langchain | Langfuse | Langgraph | Langsmith | Language Models | Large Language Models | LoRA | MCP | Machine Learning | Model Context Protocol | Modularity | Multi-Agent | Multi-Agent Systems | Observability | Orchestration | PEFT | Prompt engineering | PyTorch | Python | QLoRA | Quantization | Regression testing | Span Capture | Structured Output | System design | TensorRT-LLM | Testing | The Loop | Tool-Calling | Tracing | Trajectory Evaluation | VLLM
Education
Regions
Countries
States
Cities
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