AI Agent Builder

Key Responsibilities

• Build and configure Copilot agents and agentic workflows in Copilot Studio
• Develop integrations using Power Platform, Azure Logic Apps, and REST/APIs
• Integrate Copilot with enterprise systems: Sharepoint, Outlook, Teams, Salesforce, TM1, etc.
• Implement data mapping, transformations, and workflow logic as per architecture
• Collaborate with solution architects to convert design instructions into working solutions
• Write scripts in PowerShell / Python / C# / JavaScript

Required Skills

• Strong hands-on experience with Agent implementation in Copilot Studio, Power Automate, Power Apps, Azure Logic Apps 
• Experience with M365, Dynamics, Azure services
• REST API integration, OAuth2, Azure AD (Entra ID) authentication
• Experience with at least two enterprise integrations: Sharepoint, Outlook, Teams, Salesforce, TM1
• Ability to understand technical diagrams, integration patterns, and solution specs

About LeadSquared

LeadSquared is a leading sales execution and marketing automation platform trusted by 2,000+ businesses globally, including healthcare, education, financial services, and real estate. Headquartered in Bengaluru with offices across the US, UK, UAE, and Southeast Asia, we empower sales teams to close faster, smarter, and at scale.

Our AI team is at the forefront of integrating cutting-edge large language model capabilities into enterprise workflows — building intelligent agents, copilots, and automation systems that redefine how businesses operate.

Role Overview

We are looking for a Senior AI Engineer with hands-on experience building LLM-powered agents and agentic AI systems. You will design, develop, and deploy autonomous AI pipelines that solve complex, multi-step business problems — from lead qualification and follow-up automation to intelligent CRM workflows and beyond.

This role is ideal for someone who is deeply excited about the frontier of AI, can move fast, and wants their work to directly impact millions of sales professionals worldwide.

Key Responsibilities

•

Design and build LLM-powered agentic systems using frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI to automate complex, multi-step workflows.

•

Develop and maintain Retrieval-Augmented Generation (RAG) pipelines with vector databases (Pinecone, Weaviate, Chroma, pgvector) for domain-specific knowledge grounding.

•

Build and integrate tool-use and function-calling capabilities into AI agents, enabling dynamic interaction with internal APIs, databases, and third-party services.

•

Implement prompt engineering strategies including chain-of-thought, few-shot prompting, and structured output parsing to ensure reliable agent behavior.

•

Design evaluation frameworks and observability pipelines (LangSmith, Helicone, custom metrics) to monitor agent performance, accuracy, and cost.

•

Collaborate with product, sales, and domain teams to translate business requirements into AI-driven solutions and features.

•

Optimize LLM inference for latency and cost using techniques like caching, model distillation, quantization, and batching.

•

Stay current with the rapidly evolving LLM ecosystem and proactively propose improvements and new approaches.

•

Contribute to internal best practices, documentation, and knowledge-sharing across the engineering org.

Required Qualifications

Experience

•

2–4 years of professional software engineering experience, with at least 1–2 years focused on LLM/AI systems.

•

Proven experience shipping LLM-based products or agentic AI systems into production environments.

Technical Skills

•

Strong proficiency in Python and familiarity with async programming patterns for AI pipelines.

•

Hands-on experience with LLM APIs: OpenAI (GPT-4o), Anthropic (Claude), Google (Gemini), or open-source models (Llama, Mistral).

•

Experience with agentic frameworks: LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, or similar.

•

Solid understanding of RAG architectures, embedding models, and semantic search.

•

Experience with vector databases and similarity search infrastructure.

•

Knowledge of REST APIs, microservices architecture, and containerization (Docker/Kubernetes).

Problem-Solving & Mindset

•

Strong ability to decompose ambiguous, open-ended problems into structured AI system designs.

•

Experience with prompt debugging, LLM evaluation, and iterative refinement workflows.

•

Ability to balance research exploration with engineering pragmatism to ship reliable systems.

Preferred Qualifications

•

Experience with multi-agent orchestration and agent memory systems (short-term and long-term).

•

Familiarity with fine-tuning or RLHF workflows for domain adaptation.

•

Background in NLP, information retrieval, or conversational AI.

•

Prior experience in B2B SaaS or CRM domain is a plus.

•

Contributions to open-source AI/ML projects or published research/blogs.

•

Experience with cloud platforms: AWS, GCP, or Azure — particularly AI/ML services

Job Title: AI/ML Consultant

Company Name- WINIT

Location: Hyderabad

Duration- 3 months

Role Overview:

We are looking for a passionate and skilled AI/ML Consultant to join our dynamic team. In this role, you will play a key part in designing and implementing intelligent systems, including the development of a cutting-edge Sales Supervisor Agent. You will work on projects involving Generative AI, Voice AI, sales performance analysis, and recommendation systems that drive automation and strategic decision-making in sales operations. As a consultant, you will collaborate with cross-functional teams to understand business challenges, recommend AI-driven solutions, and deliver scalable, production-ready applications.

Project Knowledge: Generative AI & Voice AI

Experiment with Generative AI models (e.g., GPT, Claude) for tasks such as content creation, email generation, and chat-based assistance.

Build and integrate AI Voice solutions like speech-to-text, call summarization, and conversational agents using tools such as Whisper, ElevenLabs, or Dialogflow.

Integrate GenAI and Voice AI capabilities into the Sales Supervisor Agent for automation and decision support.

Location: Jaipur, Rajasthan (Work From Office)

Experience: 4+ Years

Job Type: Full-Time

We're looking for an AI Assisted Developer who is passionate about building modern applications using AI-powered coding tools. You'll leverage AI to accelerate development while delivering scalable, high-quality software solutions.

Key Responsibilities

• Build scalable web applications, APIs, and automation workflows.
• Leverage GitHub Copilot, Cursor, Claude Code, ChatGPT, and similar AI tools to improve development productivity.
• Optimize AI prompts and workflows for efficient coding.
• Apply best practices in system design, TDD, debugging, testing, and code reviews.
• Collaborate with Product, QA, and DevOps teams to deliver high-quality software.
• Stay up to date with emerging AI development tools and technologies.

Requirements

• 4+ years of software development experience.
• Proficiency in Python, JavaScript/TypeScript, or Go.
• Experience with React, Node.js, or modern development frameworks.
• Hands-on experience with GitHub Copilot, Cursor, Claude Code, ChatGPT, or similar AI coding tools.
• Strong understanding of Git, CI/CD, SQL/NoSQL, system design, and software engineering principles.
• Excellent problem-solving and communication skills.

Preferred: Experience with LangChain, LangGraph, OpenAI APIs, FastAPI, Agentic AI, MCP, Prompt Engineering, AI Automation, or AWS/Azure/GCP.

Apply Now

Application Form: https://zfrmz.com/pAKb2ynfomIsuNwRfRbV?utm_source=cutshort

Role Overview 

We are looking for an AI Engineer to design, build, and ship production AI systems, including agentic AI applications, for enterprise clients. This is a hands-on engineering role: you will write production code, build and evaluate models and agents, and work closely with architects and product teams to take solutions from prototype to scale. 

Key Responsibilities 

Design and build agentic AI systems: agent workflows, tool/function-calling, memory, and human-in-the-loop patterns. Build and productionise RAG pipelines, prompt-based applications, and LLM integrations across providers. Develop and maintain data and ML pipelines: feature engineering, model training, evaluation, and monitoring. Integrate AI systems with enterprise applications (CRMs, ERPs, ITSM tools) via APIs, events, and MCP-based tool servers. Implement guardrails, prompt-injection defences, and evaluation frameworks to keep AI systems safe and reliable in production. 

Write clean, tested, production-grade code and participate actively in code and design reviews. 

Collaborate with architects, product managers, and delivery teams to translate requirements into working AI solutions. Troubleshoot and optimise AI systems for accuracy, latency, and cost in production. 

Required Qualifications 

8–12 years of hands-on software engineering experience, with a strong, unbroken technical track record. Hands-on experience building and shipping AI/ML systems in production, not just POCs. 

Practical experience with agentic AI systems and at least one major agent framework (LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, Bedrock Agents/Strands, or Semantic Kernel). 

Experience with LLM/GenAI systems: RAG pipelines, prompt engineering, structured outputs, and tool calling across providers. 

Strong Python skills (TypeScript/Node.js a plus), with production-grade testing, CI/CD, and API design practices. Working knowledge of ML fundamentals: model evaluation, feature engineering, and experimentation. Cloud-native experience on AWS and/or Azure: containers, serverless, event backbones, and vector databases. Understanding of LLM safety and reliability practices: guardrails, prompt-injection defences, and observability. 

Design and develop Agentic AI systems using LLMs, tools, memory,

workflows, and MCP.

Build production-grade RAG pipelines, including ingestion, chunking,

embeddings, retrieval, reranking, and evaluation.

Implement context engineering strategies for improving LLM accuracy,

relevance, and reliability.

Develop and integrate MCP-based tools and services for AI agents.

Work with LLMs, SLMs, quantized models, and model optimization

techniques for efficient inference.

Develop scalable backend services and APIs for AI applications.

Design databases and data models supporting AI/agentic applications.

Implement AI observability covering latency, token usage, cost, failures,

quality, and agent/tool execution.

Apply AI governance and responsible AI practices, including security,

access control, data privacy, and auditability.

Optimize AI systems for latency, scalability, cost, and reliability.

Collaborate with engineering and product teams to take AI solutions from

POC to production.

Strong hands-on experience with GenAI, LLMs, and Agentic AI.

Experience building RAG applications.

Strong understanding of Context Engineering and prompt/context

optimization.

Role Overview

We are looking for a hands-on AI/ML Engineer to design, develop, and deploy

production-ready GenAI and Agentic AI applications. The role involves building

intelligent agents, RAG pipelines, AI APIs, backend services, and scalable AI

infrastructure with a strong focus on context engineering, observability,

governance, and model optimisation.

Key Responsibilities

Required Skills

Practical experience with MCP (Model Context Protocol).

Experience with frameworks such as LangChain, LangGraph,

LlamaIndex, or equivalent.

Knowledge of LLM/SLM deployment and quantization techniques.

Strong Python backend development experience.

Experience developing REST APIs using FastAPI/Flask or equivalent.

Strong understanding of SQL/NoSQL databases and database design.

Experience with vector databases such as Qdrant, Pinecone, Weaviate,

ChromaDB, or FAISS.

Understanding of AI observability, evaluation, monitoring, and

governance.

Experience with cloud platforms and production deployment is preferred.

Strong understanding of software engineering principles, Git, testing, and

CI/CD.

AI Engineer

We are seeking an AI Engineering specialist focused on AI evaluation, and continuous quality improvement for Ezra MetLife's employee-facing AI platform. This role will establish and scale the testing strategy for enterprise AI agents, ensuring high response quality, reliability, and production readiness. The engineer will build automated regression testing framework (preferred Playwright ), define AI evaluation methodologies, analyze AI performance metrics, and partner with engineering teams to continuously improve answer quality, grounding accuracy, and customer experience. This position is critical to enabling confidence as Ezra expands its AI agent portfolio and employee-facing capabilities.

Required Skills & Experience

• C# and .NET development experience

• Experience with at least one AI evaluation framework (e.g., prompt evaluation, RAG evaluation, LLM quality assessment)

• Microsoft Agent Framework (preferred) or similar enterprise agent frameworks

• Experience with Azure OpenAI / Azure AI Foundry

• Microsoft 365 Agent SDK

• Azure AI Search, RAG pipelines, and retrieval quality testing

• Infrastructure as Code using Terraform

• Experience building automated testing and AI quality validation processes

• Familiarity with telemetry analysis, AI observability, and performance measurement

• Strong analytical skills with a passion for improving AI response quality and reliability

Skills: AI Agents~Core .NET Technologies~C# 5.0

Experience Required: 10 & Above

Location: Hyderabad :5+ relevant exp in AI + .NET

Senior Agentic AI Engineer - (Freelance)

Positions: 2

Experience: Ideally 4(J–(J8 years with strong software-engineering fundamentals and recent hands-on Agentic AI experience.

Mission

Build UC2's governed AI agents capable of reasoning across and interacting safely with enterprise IT systems.

Mandatory capabilities

• Python
• LangGraph
• Agentic AI
• Tool/function calling
• Stateful workflows
• Structured outputs
• Human-in-the-loop
• Guardrails
• Agent state/checkpointing
• Agent evaluation
• FastAPI
• REST APIs
• Async Python

Retry/timeout/error handling

Highly desirable

MCP, LangChain, Semantic Kernel, agent observability, event-driven architecture and experience integrating AI agents with ServiceNow/Splunk/Confluence or similar enterprise platforms.

The candidate should understand how to engineer:

Location: Hyderabad, India (home base), deployed at client sites in India. Occasional Middle East exposure possible.

About the Role

You will work as a senior AI consultant who embeds inside a customer's business. Your job is to learn how the business makes money, find the highest value problem, and build a working system that solves it.

You will not hand over a document and walk away. You will show working software early, own the roadmap, own the client relationship, and stay after go-live to run and improve the system.

Four behaviors define this role:

1. Go where the work happens. You work onsite with the customer, in the room where decisions are made.
2. Show working software early. You build a prototype in days, not a document in weeks.
3. One person owns the outcome. You are the single point of accountability for the result.
4. Stay after go-live. You keep running and improving the system after launch.

You are the single point of accountability. You are not a solo builder. A full KnackLabs engineering team in Hyderabad builds and runs the production systems behind you.

This role involves extended onsite deployments at client locations in other cities, sometimes up to six months at a stretch. Please apply only if you are ready for this way of working.

What you'll own

1. Discovery - Learn how the customer makes money. Find the highest value problem to solve first.
2. The prototype - Build a working prototype fast, using real or sample data, to prove the idea.
3. The roadmap - Decide what to build, in what order, and set clear success measures tied to business outcomes.
4. The build - Design and ship the production system with the Hyderabad engineering team. This includes data integration, agents, retrieval, and evaluations.
5. The client relationship - Be the trusted technical contact for the customer, from engineers to senior leaders.
6. Go live and after - Deploy the system, watch how it performs, fix problems, and improve it over time.
7. Feedback to the product - Share what you learn in the field so our platform and internal tools get better.

What we are looking for

1. Around 7 or more years of software engineering experience, including customer-facing or client delivery work.
2. Experience working at a consulting or professional services firm in a client-facing delivery role.
3. A full-stack development experience with strength in backend technologies.
4. Strong programming skills in Python. Working knowledge of TypeScript or JavaScript.
5. Production experience with large language models, including prompt engineering and agent development.
6. You build with AI coding tools like Claude Code as your default way of working. You have built real apps and agents this way, not just used it for document generation or review.
7. Experience building retrieval-augmented generation (RAG) systems: chunking, embeddings, vector databases, retrieval, and reranking.
8. Experience building and deploying AI systems.
9. Experience integrating with APIs and enterprise systems.
10. Experience with at least one cloud platform (AWS, Azure, or GCP).
11. Experience building evaluations to measure accuracy, safety, latency, and cost.
12. Clear communication. You can explain a technical choice to an engineer and to a business leader.
13. High ownership and comfort with ambiguity. You can take an unclear problem and turn it into a plan.
14. Willingness to work onsite at client locations in India for extended periods, and to travel as the work needs.

Nice to have

1. Experience deploying AI systems in regulated industries such as insurance, banking, or the public sector.
2. Experience with on-premises or private cloud (VPC) deployments.
3. Experience with observability and tracing tools such as LangSmith or Braintrust.
4. Experience with data engineering and pipelines.
5. A history of side projects, open source contributions, or products you shipped end-to-end.
6. Experience in embedded or forward-deployed roles before.

Stack and tools

1. Languages: Python and TypeScript.
2. Models: Claude and other frontier or open source models, chosen to fit the customer.
3. AI patterns: RAG, agents, prompt engineering, and evaluations.
4. Vector and retrieval: vector databases and retrieval pipelines.
5. Cloud: AWS, Azure, or GCP, on public or private cloud.
6. Integration: REST APIs and enterprise system connectors.

As an intern for the Artificial Intelligence business automation team, you will be focusing on researching and developing AI tools that automate some parts of our business automation. As business automation is important throughout the firm, you will have the opportunity to collaborate with teams across Springer.