Machine Learning Engineer, Human-AI Interaction
XenonStack · Mohali, India ·
- Seniority
- Middle
- Category
- ML ai
- Experience
- 2+ years
- Visa sponsorship
- No
- Company size
- 51-200
XenonStack · Mohali, India ·
XenonStack is a Data and AI Foundry for Agentic Systems, enabling enterprises to design, deploy, operate, and scale intelligent agents across digital and physical environments.
We build enterprise-grade platforms across the agentic stack:
Our mission is to accelerate the world’s transition to AI + Human Intelligence by making agentic systems reliable, responsible, and enterprise-ready.
We are seeking an Machine Learning Engineer, Human-AI Interaction to design, implement, and optimize the way AI agents understand, respond, and act within enterprise workflows.
This role sits at the intersection of linguistics, context engineering, and enterprise AI systems. You will blend prompt engineering, context orchestration, and memory architecture to ensure large language models (LLMs) and multi-agent systems are accurate, reliable, and compliant in production.
If you are passionate about making AI smarter, context-aware, and enterprise-ready, this role is for you.
Prompt Engineering – Design, test, and refine prompt strategies to drive optimal agent behavior across diverse use cases.
Context Orchestration – Build pipelines integrating RAG, knowledge graphs, APIs, and layered memory (short-term, long-term, episodic).
Optimization – Optimize token usage and context allocation for long-running, multi-turn, and multi-agent workflows.
Reusable Blueprints – Develop reusable interaction templates and context blueprints for engineering and product teams.
Compliance & Guardrails – Implement safety, compliance, tone, and brand guardrails in agent workflows.
Continuous Improvement – Use execution traces, feedback, and automated evaluation to improve agent responses.
Experimentation – Conduct A/B testing on prompt and context variations to measure accuracy, latency, and cost trade-offs.
Knowledge Management – Maintain a central library of tested interaction patterns and context management strategies.
Research Tracking – Stay updated on multi-agent orchestration frameworks and the state of AI interaction design.
Must-Have
2–4 years in AI/ML engineering, NLP, or enterprise software development.
Strong understanding of LLM architectures, prompt engineering, and context window limits.
Hands-on with RAG pipelines, vector databases, and knowledge graph integration.
Proficiency in Python and frameworks like LangChain, LangGraph, LlamaIndex.
Familiarity with enterprise AI governance, privacy, and compliance standards.
Proven ability to translate business objectives into structured AI interactions.
Good-to-Have
Experience with multi-agent orchestration (MCP, A2A messaging, AgentBridge).
Knowledge of reinforcement learning (RLHF, RLAIF, reward modeling).
Exposure to edge AI deployment and quantized inference.
Domain knowledge in BFSI, GRC, SOC, or FinOps.
At XenonStack, we believe in shaping the future of intelligent systems. We foster a culture of cultivation built on bold, human-centric leadership principles, where deep work, simplicity, and adoption define everything we do.
Our Cultural Values
Agency – Be self-directed and proactive.
Taste – Sweat the details and build with precision.
Ownership – Take responsibility for outcomes.
Mastery – Commit to continuous learning and growth.
Impatience – Move fast and embrace progress.
Customer Obsession – Always put the customer first.
Our Product Philosophy
Obsessed with Adoption – Making AI accessible and enterprise-ready.
Obsessed with Simplicity – Turning complexity into seamless, intuitive AI experiences.
Be a part of our mission to accelerate the world’s transition to AI + Human Intelligence and reimagine how enterprises interact with AI agents.