AI Engineer at AI diffusion in asset-heavy & regulated industries · Chennai · 2 - 5 years · Upto ₹26L / yr · Posted 27 Oct 2026
Python
Django
FlaskAbout the company
We are an AI diffusion company. We partner with midmarket companies in asset-heavy, regulated industries to co-build deeply industry-specific agentic and machine learning solutions, which then get diffused across the industry through revenue-share and equity-linked models.
Our Past
We have built and sold two companies, one of which was an AI company. Our founders and leaders are Math PhDs, Ivy League University Alumni, Ex-Googlers, and successful entrepreneurs.
Team
Our team of 20+ employees consists of data scientists, AI/ML Engineers, and mathematicians from top engineering and research institutes such as IITs, CERN, IISc, UZH. Our team includes academicians, IBM Research Fellows, and former founders. This role focuses on healthcare and RCM, working alongside our AI/ML engineers, data scientists and domain specialists.
Work you’ll do
We are looking for an experienced AI engineer with hands-on experience in healthcare and revenue cycle management (RCM). You will build and deploy AI agents, automations and data workflows for healthcare operations, with a focus on the US market.
The work includes payer-portal automation for claim status and EOB retrieval, voice agents for payer follow-up, analysis of claim journeys and bottlenecks, and AI-assisted insights into practice performance. You will work directly with billing and operations teams, owning delivery from understanding the workflow through deployment, evaluation and ongoing improvement.
Responsibilities
- Own AI agent and automation workflows from requirements through design, development, deployment and monitoring.
- Build agents that work across payer portals, healthcare documents, practice-management systems, EHR-connected tools and voice channels.
- Turn fragmented healthcare data into validated, structured inputs for automation and analysis.
- Work directly with billing and operations teams to understand workflows, resolve exceptions and improve systems based on user feedback.
- Evaluate workflow accuracy and completion rates using representative test sets. Monitor failures and operating costs, and implement retries, exception queues and human escalation
- Implement approved privacy and security controls for sensitive healthcare data, including access restrictions, audit trails and safeguards against exposing patient information in logs or model inputs.
- Diagnose and improve failures across data ingestion, integrations, agent execution and user-facing workflows.
- Measure operational outcomes, such as time saved, reduced manual effort and faster turnaround, with the teams using the systems.
Who you are
You are a passionate and results-oriented engineer who is driven by the potential of AI/ML to revolutionize processes, enhance products, and ultimately improve user experiences. You thrive in dynamic environments and are comfortable navigating ambiguity. You possess a strong sense of ownership and are eager to take initiative, advocating for your technical decisions while remaining open to feedback and collaboration.
You are adept at working with real-world, often imperfect data, and have a proven ability to develop, refine, and deploy AI/ML models into production in a cost-effective and scalable manner. You are excited by the prospect of making US healthcare operations faster and more reliable, for the people who run them and the patients they serve.
Skills & Requirements
Must have
- At least 2 years of professional AI engineering experience, including deploying and supporting systems used in real operations. Internships and academic projects alone do not meet this requirement.
- Experience deploying and supporting an LLM-based application or AI agent in production, using capabilities such as retrieval-augmented generation (RAG), tool calling or multi-step workflows.
- Preferably has hands-on experience building AI or automation solutions for healthcare RCM workflows, such as claim status checks, accounts receivable follow-up, denial management, payment posting or payer interactions.
- Preferably has practical understanding of how billing teams work, how claims move through the revenue cycle, and how incomplete data and exceptions affect execution.
- Ability to explain your engineering contribution, how the system was evaluated, what failed and how you addressed it.
- Strong production Python and SQL skills, experience integrating APIs, and working knowledge of JavaScript/TypeScript for browser automation or application integration.
- Experience automating workflows across APIs, browsers or user interfaces, using tools such as Playwright, Selenium or UiPath, with error handling and human escalation where needed.
- Experience deploying and monitoring applications on at least one cloud platform—AWS, Azure or GCP—using containers, version control and automated testing.
- Experience handling imperfect inputs such as scanned PDFs, inconsistent portal layouts or incomplete records, with validation and exception handling.
- Experience working directly with billing or operations teams, improving systems based on their feedback, and demonstrating outcomes such as time saved, claims processed or reduced turnaround time.
- Experience handling sensitive healthcare data through appropriate access controls, secure logging and approved data-handling practices.
Preferred experience
- Experience delivering solutions for US healthcare providers, RCM companies or payer-facing operations, including working with US billing teams and payer workflows.
- Experience with practice-management or EHR integrations, payer portals, explanation of benefits (EOB) documents or electronic remittance advice (ERA) data.
- Experience analyzing claim histories, revenue-cycle bottlenecks or practice performance.
- Eperience building voice agents or integrating speech-to-text and text-to-speech services is a plus.
- Familiarity with Model Context Protocol (MCP) integrations.
- Experience with Kubernetes, serverless applications or event-driven architectures.
Working with us
We are a young company, but we believe strongly in thinking long-term, while acting with urgency. Our ethos is rooted in innovation, efficiency and high-quality outcomes. We believe the future of work is AI-augmented and boundaryless.
Here are some of our guiding principles:
- Think in decades. Act in hours. As an independent company, our moat is time. While our decisions are for the long-term horizon, our execution will be fast – measured in hours and days, not weeks and months.
- Own the canvas. Throw yourself in to build, fix or improve – anything that isn’t done right, irrespective of who did it. Be selfish about improving across the organization – because once the rot sets in, we waste years in surgery and recovery.
- Use data or don’t use data. Use data where you ought to but not as a ‘cover-my-back’ political tool. Be capable of making decisions with partial or limited data. Get better at intuition and pattern-matching. Whichever way you go, be mostly right about it.
- Avoid work about work. Process creeps on purpose, unless we constantly question it. We are deliberate about committing to rituals that take time away from the actual work. We truly believe that a meeting that could be an email, should be an email and you don’t need a person with the highest title to say that out loud.
- High revenue per person. We work backwards from this metric. Our default is to automate instead of hiring. We multi-skill our people to own more outcomes than hiring someone who has less to do. We don’t like squatting and hoarding that comes in the form of hiring for growth. High revenue per person comes from high quality work from everyone. We demand it.
Shubham Vishwakarma
Full Stack Developer - Averlon
Similar jobs (10)
The recruiter has not been active on this job recently. You may apply but please expect a delayed response.
PythonAI Engineer
LLMs, Agents & AI Services
📍 Mumbai (On-site) | Full-time | 2-4 years
About the Role:
Unico Connect is an AI-first technology partner that builds custom mobile, web, and AI products for clients across multiple geographies.
AI is core to how we design, deliver, and scale software for our customers.
We are hiring an AI Engineer for a dedicated client engagement building a complex production AI platform, working on the AI capabilities and agentic features at the core of the product.
The mandatory requirement for this role is at least one AI feature personally shipped to production for real users, with operational ownership.
The role suits someone who thinks quickly on solutioning, can take an ambiguous problem to a working prototype in days, and has the discipline to carry it through to production with predictable economics.
You will work alongside the Senior AI Engineer and the wider pod, with ownership of parts of the AI surface area of the product.
Responsibilities:
Solutioning and POCs
Translate ambiguous customer problems into working POCs at speed.
Pick the right model, framework, and architecture, and demonstrate value early before scaling investment.
LLM Application Development
Build AI features and services using LLM APIs from OpenAI, Anthropic, Google, and self-hosted open-weight models (Llama, Qwen, Mistral).
Choose the right model per use case based on cost, latency, capability, and context-window trade-offs.
Agentic System Design
Design and implement agentic workflows using LangGraph, CrewAI, AutoGen, LlamaIndex Agents, or custom orchestration.
Cover tool use, planning, memory, and multi-step reasoning appropriate to the problem.
API and Service Development
Build production AI services and APIs using Python and FastAPI.
Handle streaming responses, async processing, structured outputs, retries, and graceful degradation when models or tools fail.
Retrieval and Tool Integration
Implement RAG pipelines with vector databases (Pinecone, Weaviate, Qdrant, pgvector, Chroma), embeddings, chunking strategies, hybrid search, and reranking.
Integrate external tools, internal APIs, and document sources through tool-calling and MCP-style patterns.
Cost Analysis and Unit Economics
Model the per-request and per-user cost of every AI feature before it ships.
Track token usage, prompt caching, batching, and model-routing strategies.
Drive measurable improvements in unit economics.
Production Hardening
Add observability and tracing (LangSmith, Langfuse, OpenTelemetry), guardrails, content safety checks, prompt injection defences, and fallback behaviour.
Prompt Engineering and Evaluation
Design, test, and iterate prompts with measured outcomes.
Build evaluation harnesses for accuracy, hallucination, latency, and cost.
Run benchmarks across models and prompt variants before locking in a design.
Requirements:
AI Feature Shipped to Production (Mandatory)
Must have personally built and shipped at least one AI feature that runs in production for real users, with operational ownership.
POCs, internal demos, and one-off scripts do not qualify.
2 to 4 Years of Professional Software or AI Engineering Experience
With at least one production AI feature owned end to end.
Strong Python Proficiency and API Development with FastAPI
Comfort with type hints, async, packaging, testing, streaming responses, and authentication.
Production-grade Python, not notebook-only code.
Hands-on Depth Across the LLM and Agent Stack
Working experience with at least two of OpenAI, Anthropic Claude, Google Gemini, or self-hosted open-weight models (vLLM, Ollama, Together, Replicate).
Working familiarity with at least one agent framework (LangGraph, CrewAI, AutoGen, LlamaIndex Agents) or hand-rolled equivalent.
Working knowledge of RAG, embeddings, and vector databases (Pinecone, Weaviate, Qdrant, pgvector, Chroma).
Solutioning Speed and POC Velocity
Demonstrated ability to move from a fuzzy problem to a working prototype in days.
Strong instinct for what to build first, what to defer, and what to throw away.
Cost Discipline for Production AI
Ability to calculate, monitor, and optimise the cost of LLM APIs, tokens, embeddings, vector store usage, and infrastructure.
Treats unit economics as a first-class concern.
AWS Familiarity
Working knowledge of EC2, S3, IAM, and at least one of Bedrock, SageMaker, or equivalent.
Comfortable in a Fast-Moving Environment
Self-directed, comfortable with ambiguity, takes ownership without being asked, and ships under shifting priorities.
Strong Written and Spoken English Communication
Able to explain trade-offs to non-AI engineers, designers, product managers, and clients in plain language.
Nice to Have
- fine-tuning or LoRA, QLoRA, PEFT exposure
- MCP server authoring
- eval framework experience (LangSmith, Promptfoo, Ragas, DeepEval)
- open-source AI contributions
- multi-modal models (vision, audio)
Machine Learning (ML)PythonJob Overview
We are looking for an experienced AI/ML Engineer with strong full-stack development skills to design, develop, and deploy AI-powered applications. The ideal candidate should be proficient in Python backend development, frontend technologies, and LLM integration, with the ability to build end-to-end AI-native products, custom dashboards, and lightweight applications.
This role focuses on applying AI to real-world business problems through rapid prototyping, prompt engineering, Retrieval-Augmented Generation (RAG), and scalable application development.
Key Responsibilities
- Design, develop, and deploy end-to-end AI-powered applications.
- Build backend services and REST APIs using Python.
- Develop responsive frontends using React, Next.js, or similar frameworks.
- Integrate LLM APIs such as OpenAI, Claude, and Hugging Face into production applications.
- Build custom dashboards, internal tools, micro-apps, and AI-driven business solutions.
- Implement prompt engineering, embeddings, RAG pipelines, and context-aware AI systems.
- Rapidly prototype and validate ideas using modern AI-assisted development workflows.
- Optimize application performance, scalability, reliability, and AI usage costs.
- Work with data processing libraries such as Pandas and NumPy.
- Collaborate with product and business teams to translate requirements into functional AI solutions.
- Take ownership of features from ideation and development through testing, deployment, and production support.
Required Skills & Qualifications
- 2+ years of experience in software engineering, AI engineering, or application development.
- Strong proficiency in Python and backend development.
- Hands-on experience with React, Next.js, or similar frontend technologies.
- Practical experience integrating LLM APIs such as OpenAI, Claude, or Hugging Face.
- Understanding of REST APIs, microservices, and system design principles.
- Knowledge of prompt engineering, embeddings, and RAG architectures.
- Experience with data processing using Pandas and NumPy.
- Ability to independently build, test, and deploy full-stack applications.
- Strong problem-solving, debugging, and analytical skills.
- Ability to work in a fast-paced environment with a strong ownership mindset.
Good to Have
- Experience with LangChain, LlamaIndex, or similar AI frameworks.
- Familiarity with vector databases such as Pinecone, Weaviate, or Supabase.
- Experience building AI chat interfaces, copilots, assistants, and custom dashboards.
- Familiarity with cloud platforms, application deployment, and production monitoring.
- Experience with AI-assisted coding tools and rapid prototyping workflows.
What We Are Looking For
- A full-stack AI engineer who is comfortable working across both backend and frontend development.
- Someone focused on building practical AI-powered products rather than only training machine learning models.
- A developer who can prototype quickly, validate ideas, and iterate efficiently using a “vibe coding” approach.
- A self-driven professional who can take ownership of a feature from concept to production.
Please note that this job is only for Ahmedabad location.
PythonProduct Engineer — Role Summary
We are looking for a Product Engineer to build user-facing products that combine AI capabilities with practical applications. You will work at the intersection of software engineering and AI, developing autonomous, agent-driven systems that solve complex educational and research problems.
Key Responsibilities:
- Product Development: Design, build, and deploy production-ready applications powered by LLMs and AI agents.
- Data Engineering: Build scalable ETL/ELT pipelines to process structured and unstructured data, including text and audio, for RAG and model fine-tuning.
- Agentic Workflows: Develop multi-step AI agents with tool calling, APIs, databases, search, reasoning, and memory.
- Rapid Prototyping: Turn ideas and research concepts into interactive, production-ready applications.
- AI Integration: Use frameworks such as LangChain, LlamaIndex, AutoGen, or custom orchestrators to integrate AI into scalable systems.
- User Experience: Transform raw AI outputs into reliable, intuitive, and responsive user experiences.
- Collaboration: Work closely with ML researchers and data engineers to integrate custom and fine-tuned models.
- Observability: Monitor agent behavior, manage edge cases, reduce hallucinations, and improve reliability in production.
The ideal candidate combines strong software engineering, AI/LLM expertise, data engineering, and product thinking, with the ability to take an AI concept from prototype to production.
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
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.
PythonAbout the company
We are an AI diffusion company. We partner with midmarket companies in asset-heavy, regulated industries to co-build deeply industry-specific agentic and machine learning solutions, which then get diffused across the industry through revenue-share and equity-linked models.
Our Past
We have built and sold two companies, one of which was an AI company. Our founders and leaders are Math PhDs, Ivy League University Alumni, Ex-Googlers, and successful entrepreneurs.
Team
Our team of 20+ employees consists of data scientists, AI/ML Engineers, and mathematicians from top engineering and research institutes such as IITs, CERN, IISc, UZH. Our team includes academicians, IBM Research Fellows, and former founders. This role focuses on healthcare and RCM, working alongside our AI/ML engineers, data scientists and domain specialists.
Key Responsibilities
- Lead product ideation, solution design, and technical strategy from concept to execution.
- Drive end-to-end product development, translating business needs into scalable technical solutions.
- Build and lead engineering teams to deliver high-impact products.
- Design and develop robust data systems to process and structure unorganized data from ERP/CRM platforms.
- Leverage AI and domain expertise in healthcare/RCM to build innovative solutions.
- Drive technology-led growth, with a focus on product scalability and business outcomes.
\Required Skills & Experience
- 15–20 years of experience in technology leadership, preferably as a CTO or senior technology leader.
- Proven experience building and/ or exiting startups.
- Strong expertise in data engineering, complex data systems, and unstructured data processing.
- Experience with AI-driven products, preferably in healthcare or Revenue Cycle Management (RCM).
- Strong product thinking, system design, architecture, and end-to-end execution capabilities.
- Ability to translate business requirements into technical roadmaps and scalable products.
PythonRole Overview
We are looking for an experienced AI/ML Engineer with strong expertise in Python, Generative AI, LLMs, LangChain, and LangGraph. The candidate will be responsible for designing and developing AI-powered applications, intelligent agents, and scalable LLM-based solutions.
Key Responsibilities
- Design and develop AI/ML and Generative AI applications using Python and modern LLM technologies.
- Build LLM-based applications and AI agents using LangChain and LangGraph.
- Develop agentic workflows involving tool calling, memory, reasoning, and multi-step orchestration.
- Integrate LLMs such as OpenAI, Azure OpenAI, Anthropic, Gemini, or other foundation models.
- Develop RAG (Retrieval-Augmented Generation) pipelines using vector databases.
- Work with embeddings, prompt engineering, semantic search, and document processing.
- Develop scalable APIs and backend services using Python, FastAPI, or Flask.
- Build and integrate AI solutions with existing applications and enterprise systems.
- Deploy and maintain AI/ML solutions on cloud platforms.
- Collaborate with data scientists, software engineers, and product teams to develop business-focused AI solutions.
Required Skills
- Strong hands-on experience in Python programming.
- Strong experience in AI/ML and Generative AI.
- Hands-on experience with LLMs and LLM-based application development.
- Strong experience with LangChain and/or LangGraph.
- Experience building AI Agents / Agentic AI workflows.
- Strong understanding of RAG, embeddings, vector databases, and prompt engineering.
- Experience with vector databases such as FAISS, Pinecone, Chroma, Weaviate, or Azure AI Search.
- Experience developing REST APIs using FastAPI/Flask.
- Good understanding of Machine Learning, NLP, and deep learning concepts.
- Experience with Azure / AWS / GCP cloud platforms.
Good to Have
- Experience with multi-agent systems and agent orchestration.
- Knowledge of MLOps / LLMOps.
- Experience with Docker, Kubernetes, and CI/CD.
- Knowledge of LLM evaluation, monitoring, observability, and AI governance.
- Experience with Azure OpenAI, Azure AI Foundry, or AWS Bedrock.
PythonJob Summary
We are looking for a skilled Python Developer with experience in Generative AI and Agentic AI to develop AI-powered applications and scalable backend services.
Mandatory Skills
- Python
- Generative AI (GenAI) / Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- LangChain / LangGraph
- Agentic AI
- FastAPI
- REST API development
- Vector Databases
- Prompt Engineering
- Microservices
Key Responsibilities
- Develop and maintain Python-based applications and backend services.
- Build GenAI/LLM-powered applications and AI solutions.
- Implement RAG pipelines for document processing and information retrieval.
- Develop AI agents using LangChain/LangGraph and Agentic AI frameworks.
- Build and integrate REST APIs using FastAPI.
- Work with vector databases for embeddings, semantic search, and retrieval.
- Design and optimize prompts to improve LLM response quality.
- Develop scalable microservices and integrate AI models into applications.
- Collaborate with cross-functional teams to deliver AI-based solutions.
- Troubleshoot, test, and optimize application performance.
Required Qualifications
- 4–6 years of experience in Python development.
- Hands-on experience with GenAI/LLMs, RAG, and Agentic AI.
- Practical experience with LangChain/LangGraph, FastAPI, and REST APIs.
- Knowledge of vector databases, prompt engineering, and microservices.
- Strong problem-solving and debugging skills.
- Ability to develop and deploy production-ready AI applications.
Data SciencePythonSupport with design and build to prove out agentic AI solution flow by working with other data
scientists and engineers to build, train Large Language Model (LLM) architectures, RAG
systems, and autonomous agentic workflows
Key qualifications:
>> AI solution design & Development: Design Agentic AI solutions using RAG (Retrieval-
Augmented Generation) and orchestration frameworks like LangGraph or LangChain.
>> Model Fine-Tuning: Solid understanding and experience with Pre-train, fine-tune, and
optimize open-source like BERT, LLama, and other proprietary foundation models for domain-
specific tasks
>> Solid Stats and ML foundations and (vibe) coding skills with Python, PySpark
>> Implement validation frameworks and tracing practices (using tools like Arize) to monitor
agent behavior, guard against model drift, and ensure compliance
>> Collaborate with Engineering to deploy models securely on cloud and on-prem ecosystems
Amazon Web Services (AWS)PythonJob Summary
We are looking for an experienced AI/ML Engineer to design, develop, deploy, and maintain machine learning and AI solutions that address complex business problems. The ideal candidate should have strong hands-on experience in Python, Machine Learning, Generative AI, LLMs, and AI/ML deployment, with the ability to work across the complete AI/ML lifecycle.
Key Responsibilities
- Design, develop, and deploy scalable Machine Learning and AI models for real-world business use cases.
- Build and optimize ML pipelines covering data preparation, feature engineering, model development, evaluation, and deployment.
- Develop solutions using Generative AI, Large Language Models (LLMs), NLP, and deep learning.
- Work with LLMs, prompt engineering, embeddings, vector databases, and Retrieval-Augmented Generation (RAG) architectures.
- Integrate AI/ML models with enterprise applications and APIs.
- Fine-tune and evaluate ML/LLM models based on business requirements.
- Implement MLOps practices for model versioning, deployment, monitoring, and continuous improvement.
- Collaborate with Data Scientists, Software Engineers, Architects, Product Managers, and business stakeholders.
- Conduct model performance evaluation, optimization, and troubleshooting.
- Ensure AI solutions meet requirements around security, scalability, reliability, responsible AI, and data privacy.
- Stay current with emerging AI/ML technologies, frameworks, and industry best practices.
Required Skills
- Strong programming experience in Python.
- Strong understanding of Machine Learning algorithms, statistics, and data structures.
- Hands-on experience with ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or equivalent.
- Experience with Generative AI and LLMs such as OpenAI, Azure OpenAI, Claude, Gemini, or open-source models.
- Strong knowledge of Prompt Engineering, RAG, embeddings, vector databases, and AI agents.
- Experience with NLP, deep learning, or computer vision is an advantage.
- Experience developing and consuming REST APIs and microservices.
- Working knowledge of SQL and NoSQL databases.
- Experience with cloud platforms such as Azure, AWS, or GCP.
- Understanding of Docker, Kubernetes, CI/CD, and MLOps.
- Familiarity with Git and modern software development practices.
Preferred Qualifications
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
- 4 years of relevant experience in AI/ML engineering or a related field.
- Experience building and deploying production-grade AI/ML solutions.
- Enterprise application development experience.
- Experience with Azure OpenAI, AWS Bedrock, Vertex AI, or similar managed AI platforms.
- Experience with LangChain, LlamaIndex, Semantic Kernel, or comparable AI frameworks is a plus.
- Experience with AI/ML model monitoring, evaluation, and optimization.
What You Bring
- Strong problem-solving and analytical skills.
- Ability to translate business requirements into practical AI/ML solutions.
- Strong software engineering and debugging capabilities.
- Ability to work independently as well as collaboratively in a cross-functional environment.
- Good communication skills with the ability to explain complex AI concepts to technical and non-technical stakeholders.
Keywords
AI Engineer | ML Engineer | Machine Learning | Generative AI | LLM | Python | NLP | Deep Learning | RAG | Prompt Engineering | AI Agents | Azure OpenAI | AWS Bedrock | MLOps | TensorFlow | PyTorch | Scikit-learn | Vector Database | Cloud AI
Location: Gurugram
Work mode: Hybrid
Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon