Senior Applied AI Engineer - India / Remote

Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Mathematics, Statistics, or a related field.

6–12 years of hands-on experience in software engineering, machine learning, data science, or applied AI product development.

Strong programming skills in Python and software engineering fundamentals.

Experience building production-grade AI, ML, analytics, or optimization systems.

Strong experience with anomaly detection, forecasting, predictive analytics, recommendation systems, or optimization algorithms.

Experience designing and implementing data pipelines for analytical and AI workloads.

Experience with SQL and modern analytics platforms such as PostgreSQL, Snowflake, BigQuery, Databricks, or similar technologies.

Experience deploying AI Systems on AWS, Azure, or GCP.

Strong understanding of agent lifecycle management, memory, context management, tool orchestration, agent loops, and common agent failure modes.

Strong understanding of RAG, embeddings, vector search, hybrid search, and reranking.

Demonstrated experience using AI-assisted development tools as part of daily engineering workflows.

Experience evaluating AI-generated code, analysis, recommendations, and designs to identify risks, inaccuracies, and hidden assumptions.

Ability to independently drive initiatives from concept to production.

Experience building agentic AI systems, autonomous workflows, or AI copilots.

Experience with MCP servers, tool orchestration, and at least one modern agentic AI framework such as LangGraph, CrewAI, AutoGen, Semantic Kernel, Mistral Agents, OpenAI Agents SDK, or similar orchestration frameworks.

Experience implementing AI observability, governance, evaluation, and reliability practices.

Experience contributing to semantic layers, business ontologies, metadata systems, knowledge graphs, or enterprise data models.

Experience in FinOps, cloud cost management, SaaS management, AI cost management, observability, or enterprise technology management.

Experience working in fast-paced startup environments.

Ships high-impact forecasting, anomaly detection, optimization, and recommendation capabilities that deliver measurable customer value.

Improves recommendation quality, accuracy, explainability, and customer adoption over time.

Builds robust evaluation frameworks and quality metrics that continuously improve AI system performance.

Contributes to autonomous optimization agents that customers trust and rely on.

Balances strong engineering rigor with rapid experimentation and delivery velocity.