AI Engineer

• *AI Engineer**

• *Location:** UL Cyber Park , Calicut
• *Department:** Engineering / AI \& Machine Learning
• *Employment Type:** Full\-time

Greeting from Tranetech Software Solution LLC

About the Role

We're looking for an AI Engineer to design, build, and deploy production\-grade applications powered by Large Language Models (LLMs). You'll work on everything from prompt engineering and Retrieval\-Augmented Generation (RAG) pipelines to fine\-tuning, evaluation, and scaling AI systems that solve real business problems. This is a hands\-on role for someone who enjoys taking LLM capabilities from prototype to production.

What You'll Do

• Design, implement, and optimize RAG pipelines (chunking strategies, embeddings, vector databases, retrieval tuning, re\-ranking)
• Integrate and fine\-tune LLMs (open\-source and proprietary APIs) for specific use cases
• Build and maintain prompt engineering frameworks, evaluation harnesses, and testing pipelines to measure output quality, hallucination rates, and latency
• Develop agentic workflows and tool\-use integrations (function calling, multi\-step reasoning, orchestration)
• Own the full lifecycle of LLM features: data preparation, model selection, deployment, monitoring, and iteration
• Work with vector databases and manage embedding pipelines at scale
• Collaborate with product and data teams to translate business requirements into AI\-powered features
• Implement guardrails, safety filters, and evaluation metrics to ensure reliable and responsible AI outputs
• Optimize for cost, latency, and performance across inference infrastructure
• Write clean, well\-tested, production\-grade Python code to support all of the above
• Stay current with the fast\-moving LLM landscape and evaluate new models, tools, and techniques

Required Qualifications

• 2–5\+ years of experience in software engineering, ML engineering, or a related field
• Hands\-on experience building applications using LLMs (GPT, Claude, Llama, Mistral, Gemini, etc.)
• Practical experience implementing RAG systems, including embeddings and vector search
• Strong Python skills, with the ability to design and ship production\-quality code independently
• Familiarity with ML/AI libraries (PyTorch, TensorFlow, Hugging Face Transformers, scikit\-learn)
• Experience with LLM orchestration frameworks
• Solid understanding of prompt engineering, context window management, and token optimization
• Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker/Kubernetes)
• Familiarity with API integration and building scalable backend services

Tools \& Technologies

• **LLM Providers / Models:** OpenAI, Anthropic Claude, Meta Llama, Mistral, Google Gemini, Cohere
• **Orchestration Frameworks:** LangChain, LlamaIndex, Semantic Kernel, Haystack, CrewAI, AutoGen
• **Vector Databases:** Pinecone, Weaviate, Qdrant, Milvus, pgvector, Chroma, FAISS
• **ML/DL Frameworks:** PyTorch, TensorFlow, Hugging Face Transformers, scikit\-learn
• **Fine\-Tuning \& Optimization:** LoRA/QLoRA, PEFT, RLHF, DeepSpeed, vLLM, TensorRT\-LLM
• **Evaluation \& Observability:** RAGAS, TruLens, DeepEval, LangSmith, Weights \& Biases, Arize, PromptLayer
• **Data Processing:** Apache Spark, Airflow, Pandas, NumPy
• **Infrastructure \& Deployment:** Docker, Kubernetes, AWS (SageMaker, Bedrock), GCP (Vertex AI), Azure AI Studio, Terraform
• **APIs \& Backend:** FastAPI, Flask, REST/GraphQL APIs, gRPC
• **Version Control \& CI/CD:** Git, GitHub Actions, MLflow, DVC

Nice to Have

• Experience fine\-tuning or instruction\-tuning open\-source LLMs
• Knowledge of evaluation frameworks (RAGAS, TruLens, DeepEval, or custom eval pipelines)
• Experience with agentic architectures and multi\-agent systems
• Background in MLOps / LLMOps practices (model versioning, monitoring, CI/CD for ML)
• Understanding of AI safety, responsible AI practices, and data privacy considerations
• Experience with streaming inference and real\-time chat applications

Selection Advantage

• **Strong Python coding ability** — candidates who can demonstrate solid software engineering fundamentals in Python (clean architecture, testing, debugging, performance optimization) beyond just calling APIs will be given priority in the selection process.

Pay: ₹450,000\.00 \- ₹900,000\.00 per year

Benefits

• Health insurance
• Leave encashment
• Paid sick time
• Paid time off
• Provident Fund

Work Location: In person