<p><p><b>Job Title : </b> Generative AI / Machine Learning Engineer<br/><br/><b>Location : </b> Pune, India<br/><br/><b>Experience Required : </b> 7+ years<br/><br/><b>Notice Period : </b> Immediate / Serving Notice</p><p><b><br/></b></p><p><b>About the Role :</b></p><p><p><b><br/></b></p>We are seeking a highly skilled Generative AI / Machine Learning Engineer to design, build, and deploy advanced AI solutions for real-world applications.
This role requires deep expertise in Machine Learning, Generative AI frameworks, and RAG (Retrieval Augmented Generation) pipelines, with the ability to integrate and scale these solutions into enterprise-grade Responsibilities :</b></p><p><b><br/></b></p><p><b>Model Development & Deployment :</b></p><p><br/></p><p>- Design, train, and deploy supervised and unsupervised ML models for large-scale </p><p>applications.</p><p><br/></p><p>- Build and optimize Generative AI pipelines (LLMs, RAG architectures, AI Agents).<br/><br/></p><p>- Implement scalable data preprocessing, feature engineering, and inference AI Applications :</b></p><p><p><b><br/></b></p>- Develop solutions using LLMs (OpenAI, LLaMA, Falcon, etc.) with fine-tuning and prompt engineering.<br/><br/></p><p>- Architect and implement RAG pipelines combining vector databases (e.g., Pinecone, Weaviate, FAISS) and LLMs for enterprise knowledge retrieval.<br/><br/></p><p>- Build and deploy AI Agents for automation, decision support, and conversational & Integration :</b></p><p><br/></p><p>- Write clean, production-grade Python code following best practices.</p><p><br/></p>- Integrate ML/GenAI solutions into existing enterprise systems, APIs, and applications.<br/><br/></p><p>- Collaborate with data engineers, product managers, and DevOps teams to deliver scalable AI & Cloud :</b></p><p><br/></p><p>- Use MLOps frameworks (MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML) for experiment tracking, CI/CD, and deployment.</p><p><br/></p>- Deploy models on cloud platforms (AWS/GCP/Azure) with containerized workflows (Docker, Kubernetes).<br/><br/></p><p>- Optimize models for latency, scalability, and cost Skills & Qualifications Expertise :</b></p><p><br/></p><p>- Strong proficiency in Python and ML libraries : TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers.</p><p><br/></p><p>- Deep understanding of ML algorithms (supervised, unsupervised, deep learning).<br/><br/></p><p>- Proven hands-on experience in Generative AI frameworks (LangChain, LlamaIndex, Haystack, RAG pipelines, AI Agent Knowledge :</b></p><p><br/></p><p>- Experience with vector databases (Pinecone, FAISS, Weaviate, Milvus).</p><p><br/></p>- Familiarity with LLM fine-tuning, embeddings, and prompt optimization.<br/><br/></p><p>- Strong foundation in data preprocessing, feature engineering, and model Skills :</b></p><p><br/></p><p>- MLOps exposure (CI/CD pipelines, model monitoring, drift detection).<br/><br/></p><p>- Cloud experience with AWS (SageMaker, Bedrock), GCP (Vertex AI), Azure ML.<br/><br/></p><p>- Familiarity with API design, microservices architecture, and distributed :</b></p><p><br/></p><p>- Bachelors or Masters in Computer Science, Data Science, AI/ML, or related fields.</p><p><br/></p>- Certifications in AI/ML, Cloud, or Generative AI (preferred but not mandatory).</p><br/></p> (ref:hirist.tech)