Senior Gen AI Engineer Tier 1 college only

About the Role

We’re building the next generation of AI-powered business software, and we’re looking for people who want to shape that future with us. With Lumen, we’re reimagining how users interact with CRM — moving beyond screens, menus and dashboards to an intelligent interface where users can simply ask AI to take actions, retrieve knowledge, generate insights and get work done. With Agent Studio, we’re enabling businesses to build, test and deploy their own AI agents for real-world workflows. And with Invorto, we’re bringing AI to voice, allowing businesses to create intelligent voice agents tailored to their customer and operational use cases.

What makes this especially exciting is the stage and scale of the opportunity. You’ll get to work on genuinely hard problems across LLMs, agents, reasoning, orchestration, voice AI, evaluation, reliability and enterprise security — not as isolated experiments, but as products used in real business workflows. You’ll have the opportunity to build zero-to-one, own meaningful parts of the product end-to-end, work closely with customers, experiment rapidly, and see your work reach production at scale.

Why join now? Because the playbook for enterprise AI is still being written. You won’t just be implementing someone else’s roadmap — you’ll help define the product, architecture and experiences that become that playbook. Expect high ownership, fast iteration, hard technical and product problems, direct customer impact, and the chance to build AI systems that have to work reliably in the real world — not just in a demo.

About the Role

We are looking for a Senior AI/ML Backend Engineer to help build the core intelligence layer powering Lumen and Agent Studio. You will design and ship production-grade backend systems that integrate LLMs into real agentic workflows — taking actions, retrieving knowledge and generating insights inside a live CRM product used by real businesses. This is a hands-on, build-focused role with direct ownership of systems that ship to production.

What You’ll Do

• Design, build and scale backend services in Python that power LLM-driven and agentic features within Lumen and Agent Studio.
• Build and productionize agentic AI systems — including planning, tool use, orchestration, memory and multi-step task execution.
• Integrate LLMs into core product workflows, focusing on reliability, latency, cost and correctness at production scale.
• Build robust APIs and services that connect AI agents with CRM data, business logic and third-party systems.
• Own evaluation, testing and monitoring for AI features to ensure they behave reliably in real-world, not just demo, conditions.
• Collaborate closely with product, design and other engineers to take features from zero to one and iterate rapidly based on real usage and customer feedback.
• Work directly with customers and customer-facing teams to understand real workflows, debug issues and translate feedback into product and engineering decisions.

What We’re Looking For

• 2–4 years of professional backend engineering experience, with strong hands-on Python skills.
• Design and build LLM-powered agentic systems using frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI to automate complex, multi-step workflows.
• Should be hands-on with traditional Machine learning frameworks like Pytorch, Scikit-learn
• Solid understanding of API design, backend architecture, databases and distributed systems fundamentals.
• Familiarity with LLM orchestration concepts — prompting, tool/function calling, RAG, agent frameworks, evaluation and guardrails.
• Comfort working in a fast-paced, ambiguous, zero-to-one environment where you’ll be defining as much as building.
• Strong communication skills — this is a customer-facing role, and you will be expected to clearly articulate technical concepts, decisions and trade-offs to both technical and non-technical stakeholders, including customers.

Good to Have

• Experience with enterprise security, reliability or observability practices for AI systems.
• Prior experience working on CRM, SaaS or other enterprise business software.
• Exposure to voice AI or real-time systems.

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. 

AI / Backend Engineer — Remote (India / Overseas)

Company: Ink'd

Compensation: $1,500–$2,500 USD/month, depending on experience

Type: Independent contractor, long-term opportunity

Ink'd is building a voice-first AI platform for residential real estate transactions. Our AI assistant allows real estate agents to generate contracts, automate workflows, and complete transaction documentation through natural conversation.

We're looking for a highly capable AI/backend engineer to help scale and maintain our production platform.

Responsibilities

• Build and maintain production AI agent workflows
• Develop integrations with third-party APIs and services
• Work with LLMs, structured data extraction, and document generation
• Troubleshoot production systems and improve reliability
• Build scalable backend services and maintain databases
• Collaborate directly with the founder and engineering team

Technical experience

• Python, FastAPI, Node.js, and REST APIs
• LLM APIs, tool calling, and agent orchestration
• PostgreSQL and cloud infrastructure
• GitHub, CI/CD, debugging, and deployment
• Experience with voice AI, WebSockets, or document processing is a major plus

What we're looking for

Someone who has independently shipped production software, can troubleshoot unfamiliar systems, communicates clearly, and takes ownership of their work.

Please include your GitHub, two examples of production systems you've built, your specific contributions, monthly compensation expectations, and availability.

Applied AI Engineer

About the Role

We are building an AI-powered platform that transforms credit origination by automating manual processes and enabling lenders to underwrite more loans with greater accuracy and lower risk.

We are looking for an experienced Applied AI Engineer to design and build production-grade Agentic AI systems capable of complex, multi-step reasoning across financial data, legal documents, and other unstructured information.

As an early engineering team member, you will take ownership of core AI architecture, multi-agent orchestration, reasoning workflows, evaluation frameworks, and scalable AI infrastructure.

Key Responsibilities

• Design and develop stateful, multi-agent AI systems for complex reasoning and decision-making.
• Build and optimize LLM pipelines using frameworks such as LangChain, DSPy, and GEPA.
• Implement RAG, prompt optimization, memory management, tool integration, and human-in-the-loop workflows.
• Develop AI evaluation frameworks using LLM-as-a-Judge and scenario-based testing.
• Implement AI observability and monitoring to track accuracy, latency, cost, and system performance.
• Build ETL pipelines and work with structured and unstructured data, SQL/NoSQL databases, vector databases, and graph-based data models.
• Integrate external tools and systems using modern agent protocols such as Model Context Protocol (MCP).
• Deploy and maintain AI services on AWS or Azure.
• Implement CI/CD pipelines, containerization, and Infrastructure as Code for reliable production deployments.

Required Qualifications

• 5+ years of professional software development experience in Python or JavaScript.
• Proven experience building and deploying production-level Agentic AI systems or complex LLM reasoning applications.
• Hands-on experience with LangChain and LLMOps, including tools such as Langfuse, Weights & Biases (W&B), or Helicone.
• Strong understanding of LLMs, RAG, prompt engineering, multi-agent orchestration, memory management, and tool calling.
• Experience with DSPy, GEPA, or advanced LLM inference and optimization techniques.
• Strong data engineering fundamentals, including ETL, SQL/NoSQL databases, and vector databases.
• Familiarity with MCP and human-in-the-loop workflows.
• Experience with AWS or Azure and modern DevOps practices, including Docker, CI/CD, and Infrastructure as Code.

Good to Have

• Experience with RLHF or RLAIF.
• Knowledge of model fine-tuning techniques such as LoRA and QLoRA.
• Experience with graph databases and graph-based data models.
• Exposure to financial services, credit underwriting, risk analysis, or document-intensive AI applications.

Why Join Us?

• Work on advanced Agentic AI and complex reasoning systems beyond conventional chatbot applications.
• Build foundational AI architecture and production-grade LLM workflows.
• Take ownership of technical decisions and system design as an early engineering team member.
• Solve challenging problems at the intersection of AI, finance, and intelligent automation.

Role Overview

We are looking for a skilled Python Full Stack / Agentic AI Engineer to design, develop, and deploy AI-powered applications and intelligent agentic workflows. The ideal candidate should have strong expertise in Python, FastAPI, LLMs, RAG, LangChain/LangGraph, and modern full-stack development.

You will work on building scalable backend services, integrating Large Language Models, developing AI agents, implementing Retrieval-Augmented Generation (RAG) pipelines, and creating production-ready AI applications.

Key Responsibilities

• Design and develop scalable backend applications using Python and FastAPI.
• Build and deploy Agentic AI solutions using LLMs and agent frameworks.
• Develop multi-step and multi-agent workflows using LangChain and LangGraph.
• Design and implement RAG (Retrieval-Augmented Generation) pipelines.
• Integrate LLMs such as OpenAI, Azure OpenAI, Anthropic, Gemini, or open-source models.
• Develop prompt engineering strategies and structured LLM workflows.
• Work with vector databases and embedding models for semantic search and knowledge retrieval.
• Build APIs and microservices for AI-powered applications.
• Integrate AI services with databases, third-party APIs, and enterprise systems.
• Develop conversation memory, tool calling, function calling, and agent orchestration capabilities.
• Implement evaluation, monitoring, logging, guardrails, and error handling for AI applications.
• Optimize applications for performance, scalability, reliability, and cost.
• Collaborate with product managers, frontend developers, data engineers, and other stakeholders.
• Write clean, maintainable, well-tested, and production-ready code.
• Participate in architecture discussions, code reviews, testing, and deployment activities.

Required Skills

Programming & Backend

• Strong proficiency in Python.
• Hands-on experience with FastAPI, REST APIs, and backend development.
• Strong understanding of asynchronous programming, API design, authentication, and middleware.
• Experience with SQL/NoSQL databases.

Generative AI / Agentic AI

• Strong understanding of LLMs and Generative AI.
• Hands-on experience building AI Agents / Agentic AI applications.
• Experience with LangChain and/or LangGraph.
• Knowledge of agent orchestration, tool calling, function calling, memory, and workflow management.
• Strong understanding of prompt engineering.

RAG

• Experience designing and implementing RAG architectures.
• Knowledge of document ingestion, chunking, embeddings, vector search, retrieval, reranking, and response generation.
• Experience with vector databases such as FAISS, Chroma, Pinecone, Weaviate, Qdrant, or similar.

LLM & AI Integration

• Experience integrating commercial or open-source LLMs.
• Understanding of embeddings, context windows, temperature, token usage, and model selection.
• Experience with structured outputs and LLM-based workflows.
• Familiarity with LLM evaluation and observability is a plus.

Full Stack

• Working knowledge of HTML, CSS, JavaScript/TypeScript.
• Experience with React.js or similar frontend frameworks is preferred.
• Ability to integrate frontend applications with Python/FastAPI services.

Senior AI Engineer

Company: Techjays

Experience: 4–8 Years

Location: Coimbatore, Hybrid

Joining: Immediate or within 30 days preferred

About the Role

We are looking for a Senior AI Engineer with strong Python development skills and hands-on experience building and deploying AI-powered applications. The ideal candidate should have practical knowledge of LLMs, Retrieval-Augmented Generation (RAG), vector databases, and scalable backend systems.

You will work closely with engineering and product teams to design, develop, and deploy reliable AI solutions for real-world business problems.

Key Responsibilities

• Design, develop, and maintain scalable backend applications using Python and frameworks such as Django, Flask, or FastAPI.
• Build and integrate AI-powered applications using LLMs, RAG pipelines, and agentic AI workflows.
• Develop document processing, embedding generation, semantic search, and retrieval pipelines.
• Integrate LLM APIs and optimize prompts, response quality, latency, and cost.
• Work with vector databases and relational databases to build efficient data retrieval systems.
• Design and implement REST APIs, asynchronous processing, caching, and queuing mechanisms.
• Apply software engineering best practices, including design patterns, clean code, testing, and version control.
• Deploy and maintain applications on AWS or GCP using Docker and CI/CD pipelines.
• Monitor production systems, troubleshoot issues, and improve application performance and reliability.
• Collaborate with cross-functional teams to translate business requirements into practical AI solutions.
• Contribute to code reviews, technical documentation, and knowledge sharing within the engineering team.

Required Skills

• 4–8 years of experience in software development, with strong hands-on Python expertise.
• Strong understanding of data structures, algorithms, object-oriented programming, and design patterns.
• Experience building backend applications using Django, Flask, or FastAPI.
• Hands-on experience with LLM-based applications and API integrations.
• Practical experience implementing RAG pipelines, embeddings, and semantic search.
• Experience with vector databases such as Pinecone, Qdrant, Weaviate, or Chroma.
• Strong knowledge of SQL and relational databases such as PostgreSQL or MySQL.
• Experience with caching, message queues, asynchronous processing, and WebSockets.
• Familiarity with AWS or GCP, Docker, Git, and CI/CD pipelines.
• Understanding of API design, system architecture, application security, and performance optimization.
• Experience testing, debugging, and maintaining production-grade applications.

Preferred Skills

• Experience building agentic AI applications and multi-agent workflows.
• Familiarity with LangChain, LangGraph, LlamaIndex, or similar frameworks.
• Knowledge of Model Context Protocol (MCP) and tool-calling mechanisms.
• Experience with LLM evaluation, hallucination reduction, and output quality monitoring.
• Familiarity with fine-tuning, model selection, and inference optimization.
• Experience with LLMOps, AI observability, and production monitoring.
• Knowledge of Kubernetes and microservices architecture.

What We’re Looking For

• Strong problem-solving and analytical skills.
• Ability to take ownership of features from development through deployment.
• Practical understanding of how to build reliable AI applications beyond prototypes.
• Ability to make sound technical decisions and explain architectural trade-offs.
• Good communication and collaboration skills.
• Willingness to learn, experiment, and work on challenging AI engineering problems.

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.

Hiring AI Engineer

Exp : 5 - 8 yrs

Edu : BE/B.Tech/MCA

Work Location : Pune WFO

Skills :

Min 4 years strong programming experience in Python is a MUST.

Min 2 years hands-on experience in AI with LLMs, RAG pipelines, and AI frameworks

RAG with deep retrieval experience (most important):

Owned a RAG system end to end in production, not a demo.

Has built an MCP server or client, or built agents using LangGraph/Semantic Kernel (tool calling, multi-step workflows).

Job Title: Senior AI Engineer

Experience: 5–10 years in software engineering, with 3+ years in AI/ML engineering preferred

Employment Type: Full-time

Department: Artificial Intelligence / Engineering

Location: Bangalore

We are looking for a highly skilled Senior AI Engineer to design, develop, deploy, and scale intelligent AI-powered applications and solutions. The ideal candidate will have strong expertise in machine learning, Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and production-grade software engineering.

The candidate will work closely with product managers, data scientists, software engineers, and business stakeholders to translate complex business requirements into scalable, reliable, secure, and cost-effective AI solutions.

Key Responsibilities

1. AI Solution Design and Development

• Design, develop, and deploy end-to-end AI and machine learning solutions.
• Build intelligent applications using LLMs, Generative AI, NLP, and deep learning techniques.
• Develop scalable AI architectures and reusable components for enterprise applications.
• Evaluate AI models and select appropriate approaches based on accuracy, latency, cost, and business requirements.

2. Generative AI and LLM Engineering

• Develop LLM-powered applications using models from providers such as OpenAI, Anthropic, Google, or open-source ecosystems.
• Design and implement RAG pipelines, document processing workflows, semantic search, and knowledge retrieval systems.
• Build AI agents and multi-step workflows using appropriate orchestration frameworks.
• Implement prompt engineering, structured outputs, tool calling, and context management.
• Optimize model performance through evaluation, fine-tuning where appropriate, and inference optimization.

3. Machine Learning and Data Engineering

• Develop and operationalize machine learning models for classification, prediction, recommendation, and other business use cases.
• Work with structured, unstructured, and multimodal data.
• Build data preprocessing, feature engineering, model training, and validation pipelines.
• Apply appropriate model evaluation techniques and statistical methods.

4. Production Deployment and MLOps / LLMOps

• Deploy AI solutions using cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
• Build automated CI/CD pipelines for AI applications and model deployment.
• Implement model versioning, experiment tracking, monitoring, and performance evaluation.
• Optimize inference latency, throughput, infrastructure utilization, and operating costs.
• Establish observability for AI applications, including token usage, retrieval quality, latency, failures, and model behavior.

5. Software Engineering and System Architecture

• Develop production-quality applications and APIs using Python and modern backend frameworks.
• Design RESTful APIs, asynchronous processing systems, and microservices where appropriate.
• Integrate AI services with enterprise applications, databases, and third-party systems.
• Use relational databases, vector databases, caching, and distributed systems effectively.
• Apply software engineering best practices, including testing, code reviews, documentation, and design patterns.

6. Security, Quality, and Responsible AI

• Implement safeguards against prompt injection, data leakage, insecure tool execution, and unauthorized access.
• Apply appropriate privacy, security, and compliance controls to AI systems.
• Evaluate hallucinations, bias, reliability, and failure modes.
• Design human-in-the-loop workflows and fallback mechanisms for critical use cases.

7. Technical Leadership and Collaboration

• Lead technical design discussions and mentor junior AI engineers.
• Define engineering standards, coding practices, and AI development guidelines.
• Collaborate with cross-functional teams to deliver measurable business outcomes.
• Conduct code reviews, evaluate emerging technologies, and recommend practical innovations.
• Communicate technical trade-offs and solution architecture to both technical and non-technical stakeholders.

Required Technical Skills

Programming and Backend Development

• Strong proficiency in Python.
• Experience with FastAPI, Flask, or Django.
• Knowledge of REST APIs, asynchronous programming, data structures, algorithms, and software design principles.
• Experience with Git, automated testing, and CI/CD.

AI and Machine Learning

• Strong understanding of supervised and unsupervised learning, model evaluation, and statistical concepts.
• Hands-on experience with PyTorch, TensorFlow, or scikit-learn.
• Knowledge of NLP, embeddings, transformers, and deep learning fundamentals.

Generative AI and LLMs

• Hands-on experience building production applications using LLM APIs or open-source models.
• Strong understanding of RAG architecture, embeddings, chunking, reranking, and retrieval evaluation.
• Experience with vector databases such as Pinecone, Weaviate, Qdrant, Milvus, or pgvector.
• Familiarity with orchestration frameworks such as LangChain, LangChain, or comparable tools.
• Experience with tool calling, structured generation, agent workflows, and LLM evaluation.

Cloud and Infrastructure

• Experience with AWS, Azure, or Google Cloud.
• Working knowledge of Docker and Kubernetes.
• Familiarity with model serving, scalable APIs, monitoring, and production deployments.
• Experience with MLOps or LLMOps practices.

Databases and Data Processing

• Strong SQL skills and experience with relational databases.
• Familiarity with NoSQL databases, data pipelines, and distributed processing.
• Experience working with unstructured data, document ingestion, and retrieval systems.

Preferred Qualifications

• Experience with fine-tuning, quantization, or serving open-source LLMs.
• Experience with multimodal AI, including text, image, audio, or video.
• Knowledge of agent evaluation, workflow orchestration, and multi-agent systems.
• Experience with AI security, guardrails, governance, and responsible AI.
• Experience optimizing AI workloads for latency, scalability, and cost.
• Contributions to open-source AI projects, research, or technical publications.

Education

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.
• Equivalent practical experience and demonstrated engineering expertise may be considered.

Key Success Metrics

• Quality, accuracy, and reliability of deployed AI solutions.
• Production adoption and measurable business impact.
• Reduction in inference costs and response latency.
• RAG retrieval quality and reduction in unsupported or incorrect responses.
• System availability, scalability, and operational stability.
• Delivery timelines, code quality, and technical mentoring effectiveness.

Most sales tools help you send emails. We’re building something different.

At Salesforge, we’re creating autonomous AI agents that can:

Find the right prospects

Generate highly personalized outreach

Run conversations

And book meetings

All without human involvement.

Why this is interesting

A lot of AI products stop at “generate text.” We’re focused on outcomes.

That means solving problems like:

How do you generate messages that actually get replies?

How do you evaluate and improve agent performance over time?

How do you orchestrate millions of AI-driven interactions reliably?

How do you combine structured data + LLMs in a way that scales?

If you enjoy working at the intersection of systems + AI + real-world feedback loops, this will feel like a playground.

What you’ll be working on

You won’t be maintaining legacy systems.

You’ll be:

Designing and building core backend systems that power our AI agents

Creating APIs and services that handle high-scale, real-time workflows

Working with queues (Kafka / SQS / RabbitMQ) to orchestrate async systems

Thinking deeply about performance, cost, and reliability in AI pipelines

Shipping features end-to-end with a small, senior team

The team

We’re a small group of experienced builders. We move quickly, care about quality, and avoid unnecessary process.

No layers of management.

No long planning cycles.

Lots of ownership and autonomy.

What we’re looking for

5+ years of backend engineering experience

Strong system design fundamentals

Experience with distributed systems and async processing

Familiarity with relational and/or document databases

Clear communicator, low ego, high ownership

Why join

You’ll work on a product where the output is measurable (meetings booked, revenue generated)

You’ll have real ownership from day one

You’ll be early in building a new category (AI sales agents)

You’ll grow as fast as we do