AI/ML Engineer

Responsibilities

• Design and deploy machine learning models, Agentic AI systems, and LLM-based applications.
• Translate business challenges into scalable AI solutions aligned with defined success metrics.
• Develop RAG pipelines, embedding strategies, and vector search architectures.
• Build agentic workflows, prompt strategies, and orchestration patterns.
• Own AI/ML solutions end to end from design through deployment and operationalization.
• Evaluate model and Agent performance using automated and human-in-the-loop methods.
• Optimize AI systems for latency, cost, scalability, and reliability.
• Support deployment workflows, CI/CD pipelines, containerization, and MLOps practices to enable scalable and reliable AI system delivery.
• Design and maintain reliable data, feature, and inference pipelines with a focus on validation, lineage, monitoring, and reproducibility.
• Stay current with advancements in AI/ML, including LLMs, agentic systems, tooling, and applied best practices, and incorporate relevant innovations into team solutions.
• Collaborate with engineers, data scientists, product stakeholders, and platform teams to deliver scalable, high-impact AI solutions aligned with business and client needs.

Qualifications

• Relevant degree preferred. Advanced degree in Computer Science, Engineering, Data Science or a related field preferred.
• 2 or more years of relevant experience required.
• Experience deploying machine learning or AI applications into production environments required.
• Strong Python expertise and software engineering practices required.
• Experience building LLM applications such as RAG systems, prompt-based workflows, tool usage, and agentic AI systems preferred.
• Hands-on experience with LLM frameworks, vector databases, embeddings, rerankers, LangChain, LlamaIndex, or similar technologies preferred.
• Understanding of classical machine learning workflows, including feature engineering, model training, evaluation, error analysis, and monitoring.
• Familiarity with tools and technologies such as FastAPI, MLflow, Docker, cloud platforms, and CI/CD pipelines preferred.
• Strong understanding of evaluation methodologies for predictive ML and agentic AI-based systems preferred.
• Demonstrated curiosity, initiative, and ability to quickly learn, evaluate, and apply emerging AI tools and technologies.
• Experience working in startup, high-growth, or fast-paced product environments preferred.