Senior LLM / Generative AI / Agentic Solutions Engineer

Senior LLM / Generative AI / Agentic Solutions Engineer

Role Overview

We are seeking a Senior LLM / Generative AI / Agentic Solutions Engineer to design, build, and deploy agentic AI systems that operate reliably in production environments. In this role, you will own the full lifecycle of Generative AI capabilities—from model adaptation and retrieval to orchestration, evaluation, and operational excellence.

You will work on complex, real-world systems where agents must plan, call tools, manage context, and execute safely and efficiently within enterprise workflows.

Key Responsibilities

Agentic AI Systems

Design and implement agent-based architectures, including supervisor, planner, and executor patterns

Develop routing, memory, and context-management strategies

Implement robust tool/function calling and failure-handling mechanisms

LLM Adaptation & Deployment

Fine-tune or parameter-efficiently adapt open-source LLMs (e.g., LoRA, QLoRA, PEFT)

Optimize inference for latency, throughput, and cost

Deploy models safely into production environments

Retrieval-Augmented Generation (RAG)

Build high-quality RAG pipelines including:

Embeddings

Retrieval and re-ranking

Grounding and citation strategies

Optimize for accuracy, speed, robustness, and cost efficiency

Reliability & Structured Generation

Enforce structured outputs using schemas and validations

Implement guardrails, post-processing, and hallucination mitigation strategies

Improve system robustness and predictability

Evaluation & Quality Assurance

Create automated evaluation frameworks for LLMs and agents

Implement offline benchmarks and online monitoring

Maintain regression tests, prompt versioning, and model version control

Production Engineering

Build and operate containerized services and APIs

Implement CI/CD pipelines, observability, and reliability best practices

Define and monitor SLOs, alerts, and incident readiness

Collaboration & Leadership

Work cross-functionally with product, platform, and data teams

Integrate GenAI capabilities into user-facing and internal workflows

Mentor engineers and promote best practices in applied GenAI engineering

Must-Have Qualifications

5+ years of experience building production ML/AI systems

2+ years operating at senior or lead level

Strong Python engineering skills (testing, packaging, performance profiling)

Hands-on experience deploying LLMs and agentic AI systems in real-world applications

Proven experience with LLM adaptation and quality/safety evaluation

Production experience implementing RAG systems

Solid MLOps fundamentals: containers, CI/CD, monitoring, versioning

Backend/API development experience (REST or gRPC), including authentication and resilience patterns

Comfortable working in cloud environments (AWS, GCP, or Azure) under production constraints

Preferred / Nice-to-Have

Inference optimization experience (quantization, caching, batching, GPU serving such as vLLM or TGI)

Agent safety engineering (prompt injection defense, tool security, sandboxing, red teaming)

Advanced evaluation techniques (LLM-as-judge, preference testing, rubric-based scoring, A/B testing)

Experience operating and tuning vector databases

Workflow or event-driven orchestration tools (e.g., Temporal, Airflow, n8n, or similar)

Multi-lingual GenAI systems and internationalization best practices