Ai Ml Jobs in Gurgaon

Roles and Responsibilities

• Analyze business needs and translate them into actionable AI and automation opportunities across teams.
• Develop and document AI and automation strategies aligned with organizational goals, measurable outcomes, and operating constraints.
• Work with stakeholders to map end-to-end processes, identify bottlenecks, and define improvement targets.
• Support the evaluation of AI/ML use cases by assessing feasibility, expected value, data readiness, and integration impacts.
• Create clear requirements and success criteria for automation initiatives, including scope, dependencies, and delivery milestones.
• Partner with engineering, data, and operations teams to ensure solutions meet business workflows and compliance expectations.
• Contribute to solution design by documenting how models, automation logic, and business rules interact within current systems.
• Maintain and communicate project artifacts such as process maps, requirement documents, and stakeholder updates.
• Monitor progress and participate in iterative delivery by incorporating feedback and adjusting requirements as needed.
• Facilitate workshops and working sessions to gather inputs, resolve ambiguities, and drive consensus.

Required Qualifications

• Proven experience in business analysis within technology-driven environments, including automation or analytics initiatives.
• Strong understanding of AI and automation concepts, including how AI/ML systems function in real-world contexts.
• Demonstrated ability to map and document business processes (e.g., current state/future state) to support solution planning.
• Experience gathering requirements from multiple stakeholders and translating them into clear, testable deliverables.
• Ability to assess data and system readiness at a conceptual level (availability, quality considerations, integration points).
• Excellent communication skills with the ability to present complex ideas to both technical and non-technical audiences.
• Strong stakeholder management skills, including facilitation, negotiation, and expectation-setting.
• Comfort working in a remote environment with well-organized planning, documentation, and follow-through.
• Experience with project tracking, documentation, and structured problem-solving approaches.

Key Responsibilities

• Lead discovery and analysis activities for AI and automation initiatives, including process documentation and requirement definition.
• Define use case business requirements: objectives, user impact, operational workflow changes, and success metrics.
• Produce process maps and business workflow documentation to guide implementation and change management.
• Collaborate with technical teams to ensure requirements align with system capabilities, data constraints, and rollout plans.
• Support prioritization of initiatives by estimating value, effort, risk, and dependencies across stakeholders.
• Ensure that automation solutions are designed to fit into operational processes, including handoffs, exceptions, and governance.
• Document assumptions, open questions, and decision logs to maintain clarity throughout the delivery lifecycle.
• Validate outcomes by defining acceptance criteria, performance expectations, and measurement approaches.
• Continuously refine requirements based on stakeholder feedback, pilot results, and implementation learnings.

We're hiring an AI Prompt & Agent Developer to own behavioral slice(s) of our voice agents. That behavior splits into two categories: behavior shared across every deployment, and behavior specific to a subset of deployments. You're someone who actually enjoys looking at the data, because the data informs everything else. You'll write prompts, design subagent architectures, build evals, and push automation rates up one small, measurable win at a time.

Responsibilities

• Write and maintain the prompts that run in production. Intent classification, information extraction, availability negotiation, closing phrases, insurance verification flows, objection handling, edge-case recovery. You own behavior that touches every customer call.
• Ship iteratively against real call data. Every morning, you'll listen to failed calls from yesterday. Every afternoon, you'll deploy a fix. You’ll be using and helping to develop dashboards, call review tooling, and automated agents to accelerate the work.
• Build evaluation harnesses. You'll develop offline eval sets, run automated prompt optimization (we use GEPA-style approaches), and establish the test suites that let us ship changes without breaking live deployments.
• Human-in-the-loop onboarding. New customers come online constantly. You'll work with and iterate on our internal AI agents that translate a practice's intake form, their scheduling rules, and their quirks into an agent configuration. Every week, you'll be designing new evaluation metrics for these customers and helping to improve existing ones.
• QA and continuous improvement. You'll simulate real-world customer scenarios, measure outcomes, and monitor production agent performance so you can catch drift early and fix it fast.

What we're looking for

• You've shipped prompts that broke production. Doesn't matter if it was at OpenAI, a chatbot startup, a research lab, or your own project. What matters is that you've felt the specific pain of a prompt that worked beautifully in dev and broke the second it hit real users.
• You're meticulous and careful. Looking at data for long stretches energizes you, as long as there's a signal. You stay organized when five things are in flight. We deploy multiple times a day, and we also run healthcare workflows where a bad change costs real money for real practices. You know the difference between moving fast and breaking things.
• Writing sensibility. The best prompt engineers are good writers. You notice register, rhythm, and word choice. You can tell why "Hello, cornerside dental? This is Ava, how can I help you out today? sounds warmer than "Hello, Cornerside Dental, this is Ava. How can I help you out today" out of a TTS.
• Analytical and empirical. You are relentlessly data-driven. Before you make changes, you proactively run experiments and measure. You don't ship because "I think this is better." You justify a change with "this moved booking rate from 78.2% to 81.4% on n=412 calls."
• Comfort with code. You don't need to be a senior engineer, but you should read Python fluently and TypeScript comfortably, and you can get almost any coding task done by pairing with modern AI coding tools.

We are looking for a GenAI Engineer (LLM Engineer) to build and scale LLM-powered SaaS applications with a secure, scalable, and high-performance architecture. In this role, you will be responsible for integrating Large Language Models (LLMs) into production applications, developing intelligent AI workflows, and building solutions that leverage Retrieval-Augmented Generation (RAG), Prompt Engineering, and Vector Databases.

As an AI Integration Engineer, you will work on integrating LLM APIs, designing and optimizing RAG pipelines, managing Vector Databases, and collaborating with product and engineering teams to deliver scalable AI-powered features. You will also ensure application security, monitor performance, optimize latency and cost, and build reliable AI solutions that enhance the overall SaaS platform.

Key Responsibilities

• Integrate LLM APIs to build intelligent automation, parsing, and conversational AI solutions.
• Design and develop Retrieval-Augmented Generation (RAG) Pipelines.
• Create and optimize Prompt Engineering workflows for AI applications.
• Build, manage, and optimize Vector Databases for efficient knowledge retrieval.
• Ensure data security, compliance, and high-performance AI architecture.
• Monitor and optimize AI model quality, latency, and operational cost.
• Collaborate with Product and Engineering teams to deliver scalable AI-powered SaaS features.

Experience Required

• 3+ years of backend development experience using Python, Go, or Node.js.
• Experience building LLM Applications using OpenAI, Claude, or Hugging Face.
• Hands-on experience with LangChain or LlamaIndex.
• Strong understanding of Retrieval-Augmented Generation (RAG).
• Experience with Vector Databases such as Pinecone, Weaviate, or pgvector.
• Experience working with Cloud Platforms (AWS, GCP, or Azure).
• Knowledge of APIs, Microservices, and Prompt Engineering.

Preferred

• Experience in FinTech or SaaS domain.
• Exposure to LLMOps and Model Fine-tuning.

Skills Required

Must Have

• Model Integration.
• Retrieval-Augmented Generation (RAG) Pipelines.
• Vector Database implementation and management.
• Security and Compliance best practices.
• AI Performance Monitoring and Optimization.

Preferred

• CI/CD for AI Applications.
• Observability for AI systems.

Education & Qualification

• B.Tech / BCA or Technical Certification.