Senior AI Engineer

Required Qualifications

• Minimum 7-8 years of professional experience in software engineering, AI engineering, ML engineering, data science, or related technical roles.
• At least 2-3 years of experience in AI development, ML engineering, or data science, with a demonstrated track record of deploying machine learning models and AI solutions in production environments.
• Strong hands-on experience building production-grade AI, ML, and data-driven systems.
• Practical experience with AI agents, agentic workflows, LLM-based applications, tool-calling architectures, workflow automation, and AI orchestration patterns.
• Strong understanding of modern AI concepts, including deep learning, generative AI, LLMs, embeddings, RAG, LLM fine-tuning, and AI evaluation.
• Strong Python development experience, including experience with Python (FastAPI/Flask/Django) or equivalent frameworks.
• Some experience with React for building user-facing AI tools, internal applications, dashboards, or workflow interfaces.
• Strong knowledge of AWS, including practical experience with cloud-native architectures, Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker, and related AWS AI/ML services (the more, the better)
• Experience with advanced LLM frameworks such as LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar agent/orchestration frameworks.
• Experience with PyTorch or TensorFlow, and familiarity with Hugging Face Transformers.
• Hands-on experience using LLMs via APIs, such as OpenAI, Anthropic, Gemini, or similar providers.
• Experience with ML pipelines and MLOps, including data preparation, model training, model deployment, experiment tracking, model/version management, monitoring, evaluation, and production support.
• Experience with AI evaluation frameworks, tools, and techniques for assessing LLM outputs, RAG performance, agent behavior, model quality, safety, reliability, and regression over time.
• Knowledge or practical experience with RLHF – human-in-the-loop evaluation, preference data, reward modeling, or feedback-driven model improvement.
• Experience with vector databases and retrieval/search technologies, such as Amazon OpenSearch, Pinecone, pgvector, or similar.
• Experience building RAG systems, including document ingestion, chunking strategies, embeddings, retrieval evaluation, reranking, and grounding techniques.
• Experience with model fine-tuning, embedding models, transformer architectures, open-source LLMs, and model benchmarking.
• Knowledge of API design, microservices, event-driven systems, and cloud-based architectures.
• Good understanding of security and governance requirements for AI systems, including access control, secrets management, data privacy, audit logging, and safe handling of sensitive data.
• Experience working in cross-functional teams with engineers, product managers, data scientists, DevOps, security, and business stakeholders.
• Strong problem-solving skills and ability to turn AI prototypes into reliable, maintainable production systems.
• Strong communication skills and ability to explain technical decisions clearly to both technical and non-technical stakeholders.