AI Engineer Jobs in Pune | Generative AI & LLM Career
Job Overview
Explore AI Engineer Jobs in Pune for professionals interested in designing, developing, deploying, and scaling next-generation Artificial Intelligence solutions. Opportunities span entry-level, mid-level, senior, and lead positions across Generative AI, Large Language Models (LLMs), Machine Learning, Deep Learning, Natural Language Processing, AI Agents, RAG, Computer Vision, Cloud AI, and MLOps.
AI Engineers will build intelligent applications that combine machine learning models, foundation models, enterprise data, APIs, vector databases, and cloud infrastructure. The role may involve developing Generative AI assistants, enterprise copilots, RAG platforms, AI agents, recommendation systems, predictive applications, document intelligence solutions, and automation platforms.
Candidates with expertise in Python, PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex, LLM APIs, vector databases, AWS, Microsoft Azure, Google Cloud, Docker, Kubernetes, and MLOps are highly relevant for these AI engineering opportunities in Pune.
Senior professionals may lead enterprise AI architecture, LLM application development, model evaluation, AI platform engineering, cloud deployment, AI governance, and production optimization initiatives.
Job Title
Position: AI Engineer / Generative AI Engineer / Machine Learning Engineer
Location: Pune, Maharashtra, India
Employment Type: Full-Time / Permanent / Contract Opportunities
Work Model: On-site / Hybrid / Remote, depending on the position
Department: Artificial Intelligence / Machine Learning / Engineering
Career Level: Entry Level, Associate, Mid-Level, Senior & Lead
Primary Domain: Artificial Intelligence, Generative AI & Machine Learning
Salary Range
Indicative compensation depends on experience, specialization, employer, AI project complexity, product exposure, and depth of production engineering expertise.
| Career Level | Indicative Annual Salary |
|---|---|
| Entry / Junior AI Engineer | ₹5 – ₹10 LPA |
| AI / Machine Learning Engineer | ₹9 – ₹18 LPA |
| Senior AI Engineer | ₹16 – ₹30 LPA |
| Lead / Generative AI Specialist | ₹25 – ₹45+ LPA |
Note: These are broad indicative ranges rather than guaranteed compensation. Professionals with specialized expertise in Generative AI, LLM Engineering, RAG, AI Agents, MLOps, Cloud AI, model optimization, and enterprise AI architecture may command different compensation.
Roles & Responsibilities
- Design, develop, test, deploy, and maintain scalable Artificial Intelligence and Machine Learning applications.
- Build production-ready AI solutions using Python and modern AI/ML frameworks.
- Develop Generative AI applications using Large Language Models and foundation models.
- Build enterprise Retrieval-Augmented Generation (RAG) pipelines that combine LLMs with private organizational data.
- Develop intelligent AI assistants, enterprise copilots, conversational systems, and workflow automation solutions.
- Build AI agents and agentic workflows capable of interacting with APIs, databases, applications, and enterprise tools.
- Integrate commercial and open-source LLMs into production applications.
- Implement prompt engineering, structured outputs, tool/function calling, context management, and model orchestration.
- Develop embedding pipelines and semantic retrieval systems using vector databases.
- Implement document ingestion, parsing, chunking, embedding, indexing, retrieval, reranking, and response-generation workflows.
- Develop machine learning models for classification, regression, forecasting, recommendation, clustering, and anomaly detection.
- Build deep learning solutions using PyTorch, TensorFlow, Transformers, and neural network architectures.
- Develop NLP applications including text classification, information extraction, semantic search, summarization, and question answering.
- Work with Computer Vision models when required for image classification, object detection, OCR, and multimodal AI applications.
- Fine-tune or adapt foundation models where appropriate using efficient model customization techniques.
- Design model evaluation frameworks covering accuracy, relevance, retrieval quality, latency, robustness, hallucination, and safety.
- Expose AI capabilities through REST APIs, FastAPI, microservices, and event-driven services.
- Deploy AI/ML applications on AWS, Microsoft Azure, or Google Cloud Platform.
- Containerize and orchestrate AI workloads using Docker and Kubernetes.
- Implement MLOps and LLMOps processes for model versioning, deployment, monitoring, evaluation, and lifecycle management.
- Monitor production AI systems for model quality, data drift, latency, errors, resource utilization, and operational cost.
- Optimize inference performance and cloud infrastructure for scalability and cost efficiency.
- Apply responsible AI, privacy, security, access control, and governance practices.
- Collaborate with Data Scientists, ML Engineers, Data Engineers, Software Engineers, DevOps teams, Product Managers, and business stakeholders.
- Senior engineers may lead AI solution architecture, technical design reviews, mentoring, platform strategy, and enterprise AI initiatives.
Technical Skill Set
| Technical Area | Skills / Technologies |
|---|---|
| Programming | Python, SQL |
| Machine Learning | Scikit-learn, XGBoost, Feature Engineering, Model Evaluation |
| Deep Learning | PyTorch, TensorFlow, Keras |
| Generative AI | LLMs, Foundation Models, Prompt Engineering |
| LLM Frameworks | LangChain, LlamaIndex, Hugging Face |
| RAG | Retrieval-Augmented Generation, Embeddings, Semantic Search, Reranking |
| AI Agents | Agentic AI, Tool Calling, Function Calling, Workflow Orchestration |
| NLP | Transformers, BERT, Text Classification, NER, Semantic Search |
| Vector Databases | Pinecone, Weaviate, Milvus, FAISS, Chroma, pgvector |
| LLM Integration | LLM APIs, Open-Source Models, Model Serving |
| Computer Vision | OpenCV, CNNs, Vision Transformers |
| MLOps / LLMOps | MLflow, Kubeflow, Model Registry, Model Monitoring |
| Cloud AI | AWS AI/ML, Azure AI, Google Vertex AI |
| AWS | SageMaker, Bedrock, Lambda, S3 |
| Azure | Azure AI, Azure Machine Learning |
| Google Cloud | Vertex AI, Cloud Storage |
| API Development | FastAPI, Flask, REST APIs |
| Containers | Docker, Kubernetes |
| Data Engineering | Pandas, NumPy, Spark, Data Pipelines |
| DevOps | Git, CI/CD, GitHub Actions, Jenkins |
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, Mathematics, or a related technical discipline.
- Strong programming skills in Python.
- Solid understanding of Machine Learning, Deep Learning, algorithms, statistics, and data structures.
- Hands-on knowledge of PyTorch, TensorFlow, Scikit-learn, or similar AI/ML frameworks.
- Understanding of Generative AI, LLMs, Transformers, embeddings, and prompt engineering.
- Familiarity with RAG architecture and semantic search concepts.
- Knowledge of relational databases, SQL, and data processing.
- Understanding of REST APIs and application integration.
- Familiarity with Git and software engineering best practices.
- Understanding of model training, validation, testing, and evaluation.
- Ability to process structured and unstructured datasets.
- Knowledge of cloud computing fundamentals.
- Understanding of AI security, data privacy, and responsible AI principles.
- Strong analytical and problem-solving capabilities.
Work Experience
Entry-Level / Junior AI Engineer
Candidates may demonstrate relevant expertise through internships, academic projects, research, certifications, hackathons, or personal AI projects.
Strong fundamentals in Python, Machine Learning, Deep Learning, NLP, SQL, and Generative AI are valuable.
Hands-on projects involving LLM APIs, RAG, Hugging Face, LangChain, or cloud AI platforms can provide an advantage.
Mid-Level AI Engineer
Professionals should demonstrate practical experience developing and deploying production AI or Machine Learning applications.
Expected competencies may include Python, PyTorch/TensorFlow, LLMs, RAG, vector databases, APIs, cloud platforms, Docker, and MLOps.
Candidates should be capable of independently developing AI features and troubleshooting production issues.
Senior AI Engineer
Senior candidates should have experience architecting and delivering complex AI systems in production.
Strong knowledge of Generative AI, LLM architecture, RAG optimization, AI Agents, MLOps/LLMOps, Kubernetes, cloud AI, distributed systems, and model evaluation is highly valuable.
Senior engineers may mentor developers and contribute to AI architecture and engineering standards.
Lead / Generative AI Specialist
Lead-level professionals may own enterprise AI architecture, technical strategy, model selection, AI platform design, governance, and production scalability.
Experience designing enterprise RAG platforms, agentic AI systems, LLM applications, multimodal solutions, and cloud-native AI architectures is advantageous.
Communication Skills
- Strong verbal and written communication skills.
- Ability to explain AI, Machine Learning, and Generative AI concepts to technical and non-technical stakeholders.
- Ability to translate business requirements into practical AI solutions.
- Strong analytical and structured problem-solving ability.
- Effective collaboration with Data Science, Software Engineering, Product, Cloud, and DevOps teams.
- Ability to document AI architecture, APIs, models, prompts, experiments, and deployment processes.
- Ability to communicate model limitations, risks, performance, and evaluation results clearly.
- Strong ownership and accountability for production AI solutions.
- Ability to participate effectively in architecture reviews and technical discussions.
- Curiosity and willingness to continuously evaluate emerging AI technologies.
- Ability to balance rapid experimentation with production-quality engineering.
- Leadership, mentoring, presentation, and stakeholder-management skills for senior roles.
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