• Build and operate the AI platform infrastructure enabling developers to ship LLM-based services faster• Implement and maintain Kubernetes-based runtime environments (incl. AKS) for AI workloads• Manage infrastructure as code with Terraform (modules, environments, CI/CD automation)• Support LLM workflows: RAG, agents, prompt experimentation, evaluations, and deployment patterns• Integrate and operate tooling such as Azure AI Foundry, LiteLLM, Langfuse, MLflow• Orchestrate pipelines using Kubeflow Pipelines and/or Argo Workflows (build, deploy, evaluate)• Improve platform reliability and observability (monitoring, logging, tracing, cost/perf signals)• Collaborate closely with developers to streamline DX (APIs, templates, docs, golden paths, automation)

• Design, develop, and deploy AI/ML models and pipelines that meet mission and performance objectives. • Build, train, and fine-tune models using frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, and LangChain. • Develop and operationalize MLOps pipelines (MLflow, Kubeflow, DVC, or custom training/inference orchestration). • Implement and optimize vector databases (Milvus, Pinecone, Chroma, FAISS) and retrieval architectures (RAG, graph, hybrid). • Write clean, efficient Python code for data ingestion, feature engineering, embeddings, and inference services. • Experiment with fine-tuning and optimization of LLMs and task-specific models (LoRA, QLoRA, PEFT). • Contribute to agent-based applications using frameworks like LangGraph, AutoGen, CrewAI, or DSPy. • Integrate AI services into real-world systems via APIs, event-driven workflows, or UI copilots. • Collaborate with data engineers, software developers, and mission analysts to ensure AI models are production-ready and aligned with customer needs. • Participate in peer reviews, contribute to shared repositories, and document models and experiments for reproducibility.

• Engage with clients to understand their unique challenges and deliver tailored AI/ML solutions • Design, implement, and maintain scalable, efficient, and robust end-to-end ML/AI systems • Manage the entire ML/AI development lifecycle, including planning, pipeline management, and deployment • Provide technical support to engineering team members, promoting best practices in ML engineering • Collaborate with AI Practice Lead to inform and shape the company's AI strategic direction

• Develop, test, and deploy AI models using Python and various AI libraries • Conduct proof-of-concept (POC) projects to validate new architectures and solutions • Collaborate with the team to understand requirements and implement AI solutions • Stay updated with the latest developments in the field of AI and incorporate them into projects