Job Opportunities

AI Engineer (5-6 Years Experience)

About the Role
Location: Kochi/Chennai/Madurai | Employment type: Full-time

We're looking for an experienced AI Engineer to design, build, and ship production-grade AI systems, including Multi agent Orchestration, Agentic applications & solutions, NLP/ML models, and the infrastructure behind them.

Key Responsibilities
• Design and build AI/ML solutions, including LLM applications, retrieval-augmented generation (RAG) pipelines, agents, and classical ML models, that solve real business problems
• Take models and prototypes from experimentation to reliable, scalable production services
• Build and maintain evaluation frameworks to measure quality, latency, cost, and safety of AI systems
• Fine-tune, prompt-engineer, and optimize foundation models for domain-specific use cases
• Develop data pipelines for training, embedding, and inference workloads
• Implement MLOps practices: CI/CD for models, versioning, monitoring, drift detection, and rollback
• Conduct code and design reviews, and mentor junior and mid-level engineers
• Stay current with the AI landscape and evaluate new models, tools, and techniques for adoption

Required Qualifications
• Strong problem-solving and system-design skills.
• Strong debugging and troubleshooting capabilities.
• Ability to work in a fast-moving product environment.
• 5-6 years of professional experience in software engineering, with at least 3 years focused on AI/ML
• Strong Python skills and solid software engineering fundamentals (testing, design patterns, code quality)
• Hands-on experience building LLM-based applications: prompt design, RAG, tool/function calling, and agentic workflows
• Experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn, and with the Hugging Face ecosystem
• Working knowledge of vector databases and search (e.g., pgvector, Pinecone, Weaviate, FAISS, Elasticsearch)
• Experience deploying and serving models on a cloud platform (AWS, GCP, or Azure) using Docker and Kubernetes
• Familiarity with MLOps tooling (MLflow, Kubeflow, Airflow, or similar)
• Solid grounding in statistics, ML fundamentals, and model evaluation
• Track record of shipping AI features to production and owning them after launch
• Clear communication skills and the ability to explain technical trade-offs to non-technical stakeholders

Preferred Qualifications
• Experience fine-tuning LLMs (LoRA/QLoRA, RLHF, or preference optimization)
• Experience with LLM observability, guardrails, and red-teaming
• Knowledge of inference optimization (quantization, batching, vLLM, caching)
• Background in NLP, computer vision, or recommendation systems
• Experience with streaming and big data tools (Spark, Kafka)
• Contributions to open-source projects, publications, or technical writing
• Bachelor’s or master’s degree in computer science, Artificial Intelligence, Engineering, or a related field.

What Success Looks Like
• First 30 days: Understand our stack, data, and product goals; ship a small improvement to an existing AI system
• First 90 days: Own a production AI feature or pipeline end to end, with defined quality and reliability metrics
• First 6 months: Lead a major AI initiative, raise engineering standards for evaluation and deployment, and mentor teammates.
• First 12 months: Own our AI roadmap alongside product and leadership, scale the platform to support additional use cases, and help hire and mentor the next members of the AI team.

Tech Stack
Python, Langgraph, Hugging Face, LangChain/LlamaIndex, FastAPI, Docker, Kubernetes, AWS/GCP/Azure, MLflow, Airflow, PostgreSQL/pgvector, Redis, Git, CI/CD