Note: The job is a remote job and is open to candidates in USA. Xformics Inc. is a global product and strategic consulting company delivering AI and digital products across multiple industry verticals. The AI Engineer will design, develop, and deploy production-ready Agentic AI and LLM-powered solutions, including intelligent agents, RAG systems, MCP-integrated tools, evaluation frameworks, guardrails, and enterprise integrations.
Responsibilities
- Design and build Agentic AI and multi-agent systems incorporating planning, reasoning, routing, memory, state management, and multi-step task execution
- Design, develop, and optimize LLM prompts to improve accuracy, reliability, and business outcomes
- Build agent workflows incorporating tool/function calling, structured outputs, routing and handoffs, retries, fallbacks, and human-in-the-loop patterns
- Develop and integrate MCP (Model Context Protocol) tools and agent-callable tools to connect AI agents with enterprise systems, APIs, databases, and data sources
- Design and implement RAG solutions, including chunking, embeddings, vector databases, retrieval, reranking, grounding, and context orchestration
- Build evaluation frameworks, evaluation sets, regression tests, and prompt-testing methodologies to measure and improve agent performance, quality, and reliability
- Implement guardrails, security controls, observability, tracing, and monitoring for production AI agents
- Optimize AI systems for cost and latency through model selection/routing, caching, batching, streaming, asynchronous execution, token management, and context management
- Support the deployment, monitoring, troubleshooting, and continuous improvement of AI agents and MCP-enabled workflows
- Integrate AI solutions with enterprise APIs, databases, and third-party systems
- Collaborate with stakeholders, product managers, architects, and engineering teams to translate business and operational requirements into AI-driven workflows and solutions
- Contribute to architecture reviews, technical design, and engineering best practices for AI platforms
Skills
- 4-8 years of overall software/AI engineering experience, including 1.5+ years building LLM and Agentic AI systems that reached production users
- Strong proficiency in Python, including experience developing production-quality AI/software solutions
- Experience with one or more Agentic AI frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, OpenAI Agents SDK, or similar frameworks
- Strong understanding of agent concepts including planning and reasoning, tool/function calling, structured outputs, memory, state management, routing/handoffs, and multi-step execution
- Hands-on experience with MCP servers/tools, tool integration, API orchestration, or enterprise system connectivity
- Experience with RAG architectures, embeddings, vector databases, retrieval, reranking, grounding, and knowledge retrieval systems
- Experience working with LLM platforms/APIs such as OpenAI, Azure OpenAI, Anthropic Claude, Gemini, AWS Bedrock, or equivalent
- Experience evaluating and testing AI agents, prompts, and RAG systems using evaluation frameworks or custom evaluation approaches
- Understanding of AI guardrails, security, compliance, governance, and responsible AI practices
- Experience with cloud platforms such as AWS, Azure, or GCP and deployment/monitoring of AI solutions
- Experience integrating applications with enterprise APIs and databases
- Strong problem-solving, analytical, communication, and collaboration skills
- Experience building enterprise copilots, AI assistants, or autonomous agent ecosystems
- Experience with classical ML and MLOps practices, including XGBoost, Random Forest, gradient boosting, time-series forecasting, MLflow, feature stores, model monitoring, or retraining pipelines
- Experience fine-tuning or adapting open models such as Llama, Mistral, or Qwen, including LoRA/QLoRA, distillation, or serving with vLLM
- Experience with knowledge graphs, entity resolution, or graph-backed retrieval
- Data engineering experience with technologies such as Airflow/Dagster, dbt, Spark, Kafka, Snowflake, or Databricks
- Open-source contributions, published agent projects, or a public portfolio of working AI solutions
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