Senior Machine Learning Engineer
First seen by Alion on Sep 12, 2026.
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
Skills
Python, Machine Learning, AWS, FastAPI, LangGraph, Generative AI, MLOps, Data Science, LLM, RAG