Lead AI Engineer at Fulcrum Digital is an agile and next-generation digital acce · Pune · 8 - 9 years · ₹35L - ₹38L / yr · Posted 19 Mar 2026
Lead AI Engineer
at Fulcrum Digital is an agile and next-generation digital acce
Job Description
About the Role
We are seeking an experienced and hands-on Lead AI Engineer with 8-9 years of experience in developing, fine-tuning, and deploying machine learning and deep learning models, including Generative AI systems. The ideal candidate will have strong expertise in classification, anomaly detection, and time-series modeling, along with deep experience in Transformer-based architectures and modern LLM ecosystems.
This role requires technical leadership, architectural decision-making, and mentoring of AI engineers, while actively contributing to building scalable AI solutions. Expertise in model optimization, quantization, and Retrieval-Augmented Generation (RAG) pipelines is highly desirable.
Responsibilities
- Lead the design, development, and deployment of ML and deep learning models for classification, anomaly detection, forecasting, and natural language understanding tasks.
- Architect and build scalable AI and Generative AI solutions, including RAG pipelines for document search, Q&A, summarization, and enterprise knowledge systems.
- Design, train, and fine-tune deep learning models including RNNs, GRUs, LSTMs, and Transformer architectures (e.g., BERT, T5, GPT).
- Drive the fine-tuning and adaptation of large language models (LLMs) using techniques such as Supervised Fine-Tuning (SFT) and Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA or QLoRA.
- Apply model optimization techniques such as quantization, pruning, and efficient inference strategies to improve latency and reduce compute and memory footprint in production systems.
- Define and implement evaluation frameworks, track model performance, monitor model drift, and drive continuous model improvement.
- Lead collaboration with data engineering, backend, platform, and DevOps teams to productionize AI solutions using scalable infrastructure and CI/CD pipelines.
- Provide technical mentorship and guidance to junior and mid-level AI engineers and contribute to best practices in ML engineering.
- Ensure clean, reproducible code, maintain experiment tracking, documentation, and version control of models and datasets.
- Stay up to date with the latest advancements in LLMs, Generative AI, and AI infrastructure, and help drive adoption of new technologies.
Required Skills & Qualifications
- 8-9 years of hands-on experience in machine learning, deep learning, or data science roles.
- Strong programming expertise in Python and ML/DL libraries such as scikit-learn, pandas, PyTorch, and TensorFlow.
- Deep understanding of machine learning algorithms, deep learning architectures, and sequence/NLP modeling techniques.
- Extensive experience with Transformer models and open-source LLM ecosystems (e.g., Hugging Face Transformers).
- Hands-on experience building Generative AI applications and RAG-based systems using frameworks such as LangChain or LlamaIndex.
- Experience with model optimization and quantization techniques (dynamic/static quantization, INT8, etc.) for efficient inference.
- Strong understanding of embeddings, vector databases, and retrieval systems (e.g., FAISS, Pinecone, Azure AI Search).
- Experience with model evaluation, monitoring, and performance optimization in production environments.
- Familiarity with containerization (Docker), experiment tracking (MLflow), and CI/CD pipelines.
- Proven ability to lead technical initiatives and mentor engineering teams.
Preferred Qualifications
- Experience fine-tuning LLMs using SFT, LoRA, or QLoRA on domain-specific datasets.
- Exposure to MLOps platforms such as SageMaker, Vertex AI, or Kubeflow.
- Experience with distributed data processing frameworks like Spark and workflow orchestration tools such as Airflow.
- Contributions to research papers, technical blogs, patents, or open-source projects in ML, NLP, or Generative AI.
- Experience designing enterprise-scale AI platforms or AI-powered products.
Shubham Vishwakarma
Full Stack Developer - Averlon
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Kody Technolab Limited is seeking an experienced AI/ML Engineer to design, develop, and deploy
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strong expertise in Machine Learning, Deep Learning, Generative AI, LLMs, MLOps, and
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• Design, develop, and deploy Machine Learning and Deep Learning models for classification,
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Why Join Kody Technolab Limited?
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Visit the Website to know more about us.
Company Website - Kody Technolab | Deep Tech Company in Robotics & AI Solution
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Kody Technolab Limited is seeking an experienced AI/ML Engineer to design, develop, and deploy cutting-edge Artificial Intelligence and Machine Learning solutions. The ideal candidate will have
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Hiring for AI Engineer
Exp: 5 - 10 yrs
Edu : BE/B.Tech/MCA
Work Location : Pune / Mumbai
Skill Set:
Total experience ranging from 5–10 years in software engineering/AI roles
Min 5 years strong programming experience in Python is a MUST
Min 3.5 years hands-on experience in AI with LLMs, RAG pipelines, and AI frameworks
2+ years shipping LLM systems in production
Experience with cloud platforms (AWS/Azure/GCP)
Machine Learning (ML)
Deep Learning
Python🚀 WE’RE HIRING | AI/ML GENERATIVE AI ENGINEER
📍 Location: Remote
💼 Experience: 5+ Years
🔄 Position: Contract – Extendable
🔹 ROLE HIGHLIGHTS
➤ Build and deploy AI/ML amp; Generative AI solutions
➤ Develop LLM, RAG, NLP, Recommendation amp; Predictive solutions
➤ Work on AI Agents, Chatbots, Computer Vision amp; Content Intelligence
➤ Build ML models using PyTorch, TensorFlow, Keras amp; Scikit-learn
➤ Develop RAG solutions using LangChain, LlamaIndex, FAISS/Milvus
➤ Integrate OpenAI, Azure OpenAI, AWS Bedrock, Vertex AI amp; Hugging Face
➤ Build scalable AI APIs using Python, FastAPI/Flask/Django
➤ Contribute to Private AI amp; Smart Agentic Systems
⚙️ MUST-HAVE SKILLS
◆ Python – 3+ years
◆ AI/ML – 5+ years
◆ Generative AI – 2+ years
◆ LLMs, RAG, Embeddings , Transformers
◆ ML/DL, NLP amp; Predictive Analytics
◆ Cloud AI Platforms – Azure / AWS / GCP
◆ AI/ML Deployment | MLOps
Interview Process - F2F Round at Pune Location
Data ScienceJob Description:
We are seeking a versatile and highly skilled Lead AI/ML Engineer with deep expertise in Generative AI (GenAI) and Large Language Models (LLMs). This role requires a leader who can take full ownership of the
AI lifecycle—from initial architectural design to final production execution. You will lead the development of scalable AI-powered applications, demonstrating exceptional execution skills and the ability to deliver high-performance results under pressure in demanding production environments.
Machine Learning & LLM Capability:
End-to-End ML Engineering: Build and manage comprehensive ML pipelines, including data ingestion, preprocessing, training, and evaluation using frameworks like PyTorch, TensorFlow, and Scikit-learn. Advanced LLM Systems: Design and implement sophisticated LLM-based applications such as autonomous agents, chatbots, and complex automation tools.
Generative AI Specialization: Architect and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases like FAISS, Pinecone, or Weaviate.
Model Optimization: Fine-tune open-source and proprietary models (e.g., LLaMA, GPT) using advanced techniques like LoRA, QLoRA, or instruction tuning.
Agentic Frameworks: Develop complex agentic workflows utilizing frameworks such as LangChain or LlamaIndex.
Prompt Engineering: Implement expert-level prompt engineering, tool/function calling, and structured output generation.
Project Ownership & Execution
Full Lifecycle Ownership: Take complete accountability for the full ML and GenAI lifecycle, spanning data processing, model development, monitoring, and optimization.
Architectural Leadership: Drive strategic architectural decisions for AI platforms, ensuring they are modular, scalable, and maintainable.
Execution Excellence: Write clean, high-performance Python code following strict OOP principles and manage CI/CD pipelines for seamless project execution.
Leadership & Mentoring: Act as a key technical leader, managing stakeholders and mentoring team members to ensure all project milestones are met with quality.
System Integrity: Manage model and prompt versioning, experiment tracking, and comprehensive documentation for all pipelines and workflows.
Performance Under Pressure
Production Reliability: Ensure all AI systems maintain extreme scalability and performance under heavy production workloads, including both batch and real-time processing.
High-Pressure Optimization: Rapidly optimize inference latency and system costs for ML and LLM systems to meet urgent business and technical requirements.
Proactive Problem Solving: Apply strong analytical thinking to address complex challenges such as system drift, hallucinations, and latency in fast-paced environments.
Robust Guardrails: Implement and manage strict evaluation frameworks and feedback loops to maintain system quality under stress.
Qualifications:
Bachelor’s or Master’s degree in Computer Science, AI, ML, or a related field.
Proven expertise in Python, system design, and scalable AI/ML architecture.
Deep knowledge of NLP, Computer Vision, and Deep Learning models.
Hands-on experience with Docker, Kubernetes, MLOps, and major cloud platforms (AWS, GCP, or Azure).
Machine Learning (ML)About the Role We are seeking a highly technical, hands-on Senior AI/ML Tech Lead to drive the design, development, and deployment of cutting-edge Generative AI applications. In this dual-impact role, you wi l act as a primary individual contributor architecting core AI engines while simultaneously leading a team of engineers through task alocation, code reviews, and technical mentorship. The ideal candidate bridges the gap between state-of-the-art AI research (LLMs, Agentic frameworks, Advanced RAG, OCR) and production-grade ful-stack engineering (Python, FastAPI, React).
Key Responsibilities
Technical Leadership & Team Management (40%)
● Technical Oversight: Lead a team of AI, backend, and ful-stack engineers; alocate tasks, establish sprint priorities, and ensure timely delivery.
● Code Quality & Reviews: Conduct rigorous code reviews to maintain high engineering standards, security, performance, and scalability across AI and fu l-stack codebases.
● Architecture & Governance: Design end-to-end system architectures for AI solutions, ensuring seamless integration between frontend interfaces, backend APIs, and AI models.
● Mentorship: Guide and upskil team members on modern software practices, LLM engineering, and agentic design patterns. Hands-On Engineering & Development (60%)
● Generative AI & Agentic Systems: Architect, build, and optimize LLM-powered applications, multi-agent workflows (e.g., CrewAI, AutoGen, LangGraph), and autonomous AI agents.
● RAG & OCR Pipelines: Design and deploy advanced RAG (Retrieval-Augmented Generation) architectures and document processing pipelines utilizing OCR techniques (e.g., LayoutLM, PaddleOCR, Tesseract, Vision LLMs) to extract structured data from unstructured sources.
● Backend Systems: Build robust, asynchronous, high-throughput microservices and RESTful APIs using Python and FastAPI.
● Frontend Integration: Colaborate on or build modern web interfaces using React (e.g., Control Towers, operations dashboards, interactive chat interfaces).
● MLOps & Vector DBs: Oversee model deployment, prompt engineering, fine-tuning, vector database integration (Pinecone, Qdrant, Chroma, PGVector), and cloud infrastructure setup (Azure/AWS).
Required Qualifications & Skills
● Overall Experience: 8 to 10 years of professional software engineering experience.
● AI/ML Domain Experience: 3 to 4+ years of dedicated, hands-on experience building and deploying AI/ML, OCR, and Generative AI solutions in production.
● Core Technical Stack: ○ Generative AI & LLMs: Extensive experience with commercial and open-source LLMs (OpenAI, Anthropic Claude, Llama), Agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI), and LLM evaluation frameworks (LangSmith, TruLens, Ragas). ○ RAG & Unstructured Data: Strong knowledge of hybrid search, re-ranking, chunking strategies, vector databases, and document inte ligence workflows. ○ OCR & Vision Techniques: Hands-on experience with OCR engines (Tesseract, PaddleOCR, Azure Document Inteligence) and Multi-Modal/Vision LLMs for document extraction. ○ Backend: Deep expertise in Python and asynchronous frameworks (FastAPI, AsyncIO). ○ Frontend: Working proficiency in React (TypeScript/JavaScript) for building interactive web UI components. ○ Cloud & DevOps: Hands-on experience with cloud platforms (Azure / AWS), Docker, Kubernetes, and CI/CD pipelines.
Preferred / Good-to-Have Skills
● Experience with cloud-native data platforms (e.g., Microsoft Fabric, Snowflake, Azure SQL).
● Familiarity with cost optimization and latency reduction techniques for LLM inference (caching, semantic routing, model quantization).
● Prior experience in client-facing technical leadership or agile consulting environments.
What We Offer
● Opportunity to lead and build high-impact, state-of-the-art Generative AI systems.
● Colaborative engineering culture with room for technical ownership and direct business impact.
● Flexible work arrangements and competitive compensation package.
Amazon Web Services (AWS)Senior Generative AI Engineer
Employment Type: Permanent with VDart Digital
Work Location: Marathalli, Bengaluru
Job Description
We are seeking a highly skilled Senior Generative AI Engineer with strong expertise in designing, developing, and deploying enterprise-scale AI solutions using Large Language Models (LLMs) and modern Generative AI frameworks. The ideal candidate should have hands-on production experience building scalable GenAI applications, AI agents, autonomous workflows, and Retrieval-Augmented Generation (RAG) systems in cloud-native environments.
This role requires deep technical expertise in LLM orchestration, AI application architecture, prompt engineering, vector databases, MLOps, and production deployment of AI systems. Candidates should have proven experience delivering real-world AI solutions in enterprise environments with strong exposure to cloud platforms and DevOps practices.
Key Responsibilities
- Design, build, and deploy enterprise-grade Generative AI applications using Large Language Models (LLMs).
- Develop intelligent AI agents and autonomous workflows using frameworks such as LangChain, CrewAI, LangGraph, AutoGen, or similar agentic AI frameworks.
- Implement and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases and semantic search technologies.
- Work extensively on prompt engineering, tool calling, memory management, agent orchestration, and multi-agent systems.
- Integrate and manage LLMs such as OpenAI, Azure OpenAI, Claude, Gemini, Llama, Mistral, or similar foundation models.
- Develop scalable AI services and APIs using Python and FastAPI.
- Build production-ready AI solutions with high availability, scalability, monitoring, and observability.
- Deploy and manage AI applications in cloud-native environments using Docker and Kubernetes.
- Collaborate with Data Science, ML Engineering, and DevOps teams to operationalize AI solutions.
- Implement CI/CD pipelines and automated deployment processes for AI workloads.
- Monitor model performance, latency, reliability, and operational efficiency in production environments.
- Ensure AI solutions follow enterprise security, governance, and responsible AI standards.
- Evaluate and adopt emerging Generative AI tools, frameworks, and models.
Required Skills
Generative AI & LLM Expertise
- Strong hands-on experience with Generative AI and Large Language Models (LLMs).
- Production-level experience building and deploying GenAI applications.
- Expertise in LangChain, CrewAI, LangGraph, AutoGen, or similar frameworks.
- Experience with AI agents, autonomous workflows, and multi-agent architectures.
- Strong understanding of prompt engineering, embeddings, model evaluation, and LLM orchestration.
- Experience integrating OpenAI, Azure OpenAI, Claude, Gemini, Llama, Mistral, or similar models.
RAG & Vector Databases
- Strong experience implementing RAG pipelines and semantic retrieval systems.
- Experience with vector databases such as Pinecone, Weaviate, ChromaDB, FAISS, or Milvus.
- Understanding of chunking strategies, embeddings, indexing, reranking, and retrieval optimization.
Python & AI Development
- Strong proficiency in Python.
- Experience with FastAPI for AI service and API development.
- Experience with AI/ML libraries and data processing tools such as Pandas and NumPy.
Cloud & Production Deployment
- Mandatory production experience on at least one cloud platform:
- Microsoft Azure
- Experience deploying scalable AI applications in enterprise production environments.
- Hands-on experience with Docker, Kubernetes, Jenkins, Terraform, and CI/CD pipelines.
- Strong understanding of MLOps, AI deployment lifecycle, monitoring, and observability.
Engineering & Operational Excellence
- Strong understanding of software engineering best practices.
- Experience with Git, version control, automated testing, and release management.
- Experience building secure, scalable, and high-performance AI solutions.
- Ability to troubleshoot production AI systems and optimize performance.
Preferred Skills
- Experience with AI observability and evaluation frameworks.
- Exposure to fine-tuning, PEFT, LoRA, or model optimization techniques.
- Experience with enterprise AI governance and responsible AI practices.
- Knowledge of distributed AI systems and scalable inference architectures.
- Familiarity with AI security and compliance standards.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
- 3–8 years of overall software engineering experience.
- Minimum 3+ years of hands-on experience in Generative AI and LLM-based application development,
- Proven track record of delivering enterprise-scale AI solutions in production environments.
- Strong communication and stakeholder management skills.
PythonMachine Learning (ML)Job Summary/ Job Opportunity:
This is an excellent opportunity for an ideal candidate with a high level of technical proficiency and meeting the below mentioned criteria -- • Strong experience in Machine Learning, Deep Learning, Generative AI, and Large Language Models (LLMs). • Hands-on experience building and deploying production-grade solutions using Azure OpenAI, OpenAI, LangChain, LangGraph, Semantic Kernel, LlamaIndex, and Agentic AI frameworks. • Strong expertise in Python, API development, microservices, and cloud-native architectures. • Experience designing and implementing RAG solutions, vector databases, embeddings, knowledge retrieval systems, and AI copilots. • Experience with Azure cloud services, MLOps, CI/CD pipelines, monitoring, and model lifecycle management. • Strong understanding of AI governance, responsible AI, security, compliance, and model evaluation frameworks. • Ability to lead technical discussions, provide architectural recommendations, mentor team members, and interact with business stakeholde
Key Objectives and Major Responsibilities:
• Design, develop, and implement scalable AI/ML and Generative AI solutions for enterprise applications. • Lead development of intelligent applications leveraging LLMs, RAG pipelines, AI agents, and document intelligence solutions. • Collaborate with business stakeholders, architects, and product teams to translate business requirements into technical solutions. • Design and optimize data pipelines, vector search solutions, embeddings, and retrieval mechanisms. • Build and maintain REST APIs, microservices, and cloud-native AI applications. • Ensure best practices in coding standards, performance optimization, security, scalability, and maintainability. • Drive AI solution deployment using MLOps practices, CI/CD pipelines, monitoring, and observability frameworks. • Perform code reviews, mentor junior developers, and contribute to capability building within the team
Key Capabilities and Competencies:
Knowledge, Skills, Qualification and Experience
• Degree in B.Tech/M.Tech (Computer Science/IT/Data Science) or related discipline preferred, with 3–4 years of relevant experience in AI/ML, GenAI and total 5-7 years of experience. • Proficiency in Python and hands-on experience with ML libraries (scikit-learn, TensorFlow, PyTorch) and GenAI frameworks/tools. • Strong understanding of machine learning, deep learning, LLMs, prompt engineering, and techniques like RAG and fine-tuning. • Experience with data processing, embeddings, vector databases, APIs, and building scalable AI driven applications. • Good communication skills, ability to work on multiple projects, and eagerness to learn and adapt to evolving AI technologies.
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
Strong AI Engineer / Machine Learning Engineer profiles.
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Mandatory (Experience 1) – Must have minimum 3+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.
3
Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.
4
Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.
5
Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.
6
Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.
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Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.
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Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.
9
Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.
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Mandatory (Age) - Candidate's Age should be below 28 Years
Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon