Leveraging Large Language Models (LLMs) and cutting-edge AI techniques to create transformative solutions for public safety and intelligence workflows.
Requirements
- 5+ years of hands-on experience in applied AI, NLP, or ML engineering (with at least 2 years working directly with LLMs, RAG, semantic search and Agentic AI).
- Deep familiarity with LLMs (e.g. OpenAI, Claude, Gemini), prompt engineering, and responsible deployment in production settings.
- Experience designing, building, and optimizing RAG pipelines, semantic search, vector databases (e.g. ElasticSearch, Pinecone), and Agentic or multi-agent AI workflows in in large scale production setup.
- Strong proficiency with modern ML frameworks and libraries (e.g. LangChain, LlamaIndex, PyTorch, HuggingFace Transformers).
- Ability to design APIs and scalable backend services, with hands-on experience in Python.
- Experience building, deploying, and monitoring AI/ML workloads in cloud environments (AWS, Azure) using services like AWS SageMaker, AWS Bedrock, AzureAI, etc.
- Familiarity with MLOps practices, CI/CD for AI, model monitoring, data versioning, and continuous integration.
Responsibilities
- Design, build, and optimize AI-powered solutions using LLMs, RAG pipelines, semantic search, GraphRAG, and Agentic AI architectures.
- Implement and experiment with the latest advancements in large-scale language modeling, including prompt engineering, model fine-tuning, evaluation, and monitoring.
- Collaborate with product, backend, and data engineering teams to define requirements, break down complex problems, and deliver high-impact features aligned with business objectives.
- Inform robust data ingestion and retrieval pipelines that power real-time and batch AI applications using open-source and proprietary tools.
- Integrate external data sources (e.g., knowledge graphs, internal databases, third-party APIs) to enhance the context-awareness and capabilities of LLM-based workflows.
- Evaluate and implement best practices for prompt design, model alignment, safety, and guardrails for responsible AI deployment.
- Stay on top of emerging AI research and contribute to internal knowledge-sharing, tech talks, and proof-of-concept projects.
Other
- Individual contributor (IC) role that emphasizes technical depth, experimentation, and hands-on engineering.
- Participate in all phases of the AI solution lifecycle, from architecture and design through prototyping, implementation, evaluation, productionization and continuous improvement.
- Author clean, well-documented, and testable code; participate in peer code reviews and engineering design discussions.
- Proactively identify bottlenecks and propose solutions to improve system scalability, efficiency, and reliability.
- Excellent problem-solving, collaboration, and communication skills; able to work effectively across remote and distributed teams.
