Designation : Senior AI Engineer (LLM Integration, ML Ops & Research)
Location : Pune (Onsite)
Experience : 4 to 6 Years
About the Role :
Were building an AI Innovation Core within Dynamisch a fast-moving team dedicated to exploring real-world applications of AI, automation, and large language models (LLMs).
As a Senior AI Engineer, youll bridge the gap between research and productization turning experimental models into production-ready AI tools. Youll collaborate directly with the CTO, lead our growing AI team, and play a key role in shaping Dynamischs AI-first ecosystem.
If you enjoy experimenting, building, and deploying AI solutions that create real business impact, this role is made for you.
Key Responsibilities
LLM Engineering & Integration :
- Design, build, and optimize AI / LLM-based solutions using OpenAI, Anthropic, HuggingFace, Mistral, or Gemini APIs
- Develop prompt pipelines, AI agents, and tool-calling workflows using LangChain / CrewAI / LangGraph / MCP Servers
- Work with retrieval systems (RAG), embeddings, and vector databases like FAISS, Pinecone, or Qdrant
Data Engineering & ML Ops :
Build and maintain ETL and data pipelines for model training and inferenceDeploy AI models using FastAPI, Docker, and cloud platforms (AWS, Azure, or GCP)Implement model versioning, dataset management, and inference optimizationAutomate training and evaluation pipelines for consistency and scalabilityResearch & Experimentation :
Fine-tune open-source models (LoRA, QLoRA, PEFT)Benchmark and compare open-source vs. API-based modelsDocument experiments and share internal research insightsStay updated with emerging frameworks like vLLM, Ollama, and HuggingFace TransformersMentorship & Collaboration :
Guide junior engineers in Python, data workflows, and AI techniquesCollaborate with frontend and backend teams to integrate AI componentsParticipate in internal demos and contribute to the Dyna-AI Framework initiativeCore Technical Skills :
Proficiency in PythonHands-on with LangChain, LlamaIndex, CrewAI, vLLM or similar frameworksExperience with FastAPI, Docker, GitHub ActionsKnowledge of vector databases (FAISS, Pinecone, Qdrant)Familiarity with HuggingFace Hub, transformers, datasets, and tokenizersUnderstanding of model fine-tuning (LoRA, QLoRA, PEFT, SFT)Exposure to cloud AI services (AWS Sagemaker, Azure OpenAI, GCP Vertex)Good to Have :
Knowledge of MLOps tools (MLflow, DVC, Weights & Biases)Experience in frontend integration (Streamlit, Gradio, ReactJS)Awareness of data security (HIPAA / GDPR) and compliance standardsContributions to open-source AI projectsWhy Join Us :
Build real AI systems used in healthcare, fintech, and automation domainsWork directly with the CTO and senior engineering teamsAccess to GPU infrastructure, paid API credits, and live datasetsFreedom to experiment, publish, and innovateFast decisions, open culture, and visible impact(ref : hirist.tech)