Looking for immediate Joiners or short notice period candidates

Role Overview :

We are looking for a Senior ML Engineer with 5+ years of experience to build, productionize, and operate ML and Generative AI solutions for our AI-powered investing advisor and wealth management platform.

The role combines ML Engineering, financial ML, GenAI, and MLOps/LLMOps, with ownership across the ML lifecycle - from experimentation and evaluation to deployment, monitoring, optimization, and continuous improvement.

Key Responsibilities :

- Build and productionize ML and AI solutions for wealth management and investment use cases, including portfolio optimization, asset allocation, risk modeling, forecasting, investor personalization/recommendations, financial NLP, RAG, and AI agents.

- Develop scalable ML/AI pipelines for training, evaluation, deployment, inference, monitoring, and retraining.

- Own key aspects of MLOps/LLMOps, including CI/CD, automated testing, model/prompt versioning, deployment, monitoring, and lifecycle management.

- Build LLM evaluation and observability capabilities covering quality, hallucination, reliability, latency, cost, and model/data drift.

- Develop production-grade ML services, APIs, and inference systems using Python, FastAPI/Django, and LangGraph or similar frameworks.

- Build and operate AI infrastructure using AWS, Hetzner, Docker, Terraform, and Jenkins.

- Support self-hosted LLMs on GPU infrastructure, including model serving with vLLM/TGI and fine-tuning using LoRA/QLoRA.

- Work closely with Data Scientists, Software Engineers, and Investment Domain Experts to take AI solutions from experimentation to production.

- Ensure AI systems meet requirements for security, governance, privacy, model risk, reliability, and responsible AI.

Qualifications & Key Skills :

- Bachelor's/Master's degree in Computer Science, Engineering, Data Science, or related field.

- 5+ years of experience in ML Engineering, AI Engineering, MLOps, or related roles.

- Strong Python and software engineering skills with experience building production ML/AI systems.

- Hands-on experience with ML model development, deployment, monitoring, evaluation, and lifecycle management.

- Experience applying ML to problems such as prediction/forecasting, classification, ranking/recommendation, risk modeling, optimization, or personalization.

- Practical experience with LLMs, RAG, embeddings, vector databases, LLM evaluation, and AI agents.

- Strong understanding of MLOps/LLMOps, CI/CD, Docker, Git, APIs, and automated testing.

- Experience with AWS, Terraform, Jenkins, and production infrastructure.

- Experience with GPU infrastructure and self-hosted LLM serving such as vLLM/TGI; exposure to LoRA/QLoRA is desirable.

- Familiarity with MLflow, Langfuse, Airflow/Kubeflow, or similar tools.

- Experience with FastAPI/Django and LangGraph is desirable.

- Understanding of AI governance, security, data privacy, model risk, and responsible AI.

- Experience in Banking & financial services, investment management, wealth management, or fintech is an advantage.

Tech Stack : Python, ML/GenAI, RAG, LLMs, AI Agents, LangGraph, MLflow, Langfuse, FastAPI/Django, Jenkins, AWS, Hetzner GPU, Docker, Terraform, vLLM/TGI, LoRA/QLoRA