AI/ML Engineer Specialist

Money Forward India; Chennai District; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Money Forward India is seeking a hands-on AI/ML Engineer to design, build, and deploy intelligent systems solving real-world product and business problems. You will work across the full AI lifecycle from data prep to deployment, focusing on robust, scalable solutions in collaboration with cross-functional teams.

The role emphasizes model development, experimentation, and performance optimization, with responsibility for maintaining reproducible pipelines and production-grade software in an Agile

Qualifications

• Proven ability to develop and evaluate ML models for business use cases.
• Strong Python coding skills with data manipulation libraries.
• Hands-on experience with TensorFlow or PyTorch.
• Familiarity with NLP and transformer-based architectures.
• Experience deploying AI services and practicing MLOps.

Responsibilities

• Design, build, and optimize ML models for production use.
• Conduct structured experiments and hyperparameter tuning.
• Prepare data and create modeling-ready datasets.
• Implement training and fine-tuning workflows with modern AI frameworks.
• Deploy models to production and monitor performance.

Skills

ML model development

Python (Pandas NumPy SciKit)**

TensorFlow PyTorch

NLP concepts

MLOps

Tools

MLflow

AWS

Job description

Role summary

We are seeking a hands-on Specialist AI/ML Engineer to design, build and deploy intelligent systems that solve real-world product and business problems. This role requires strong expertise in model development, experimentation, and performance optimization, along with the ability to translate data into scalable AI Solutions. You will work across the full AI lifecycle - from data preparation and model development to evaluation, deployment collaboration, and continuous improvement. The ideal candidate brings strong engineering discipline, experience operationalizing models, and the ability to work closely with cross-functional teams in a fast-paced environment. This is an individual contributor role focused on building high-quality AI systems that are accurate, reliable, and production-ready.

Key Responsibilities

• Model Development & Experimentation – Design, develop, and optimize machine learning models for business use cases such as prediction, classification, forecasting, anomaly detection, and NLP tasks
• Conduct structured experimentation, hyperparameter tuning and performance benchmarking
• Transform structured and unstructured data into modeling-ready datasets
• Implement training and fine-tuning workflows using modern AI frameworks
• Define and track appropriate evaluation metrics to ensure robustness and generalization
• AI System & Applied Intelligence – Contribute to AI-Powered capabilities such as search, summarization, and intelligent assistance
• Support use cases involving LLMs, embeddings, and retrieval-based architectures when required
• Evaluate and compare different modeling approaches to improve accuracy and efficiencyImprove system performance in terms of reliability, latency and cost
• Deployment & Operational Readiness – Collaborate with engineering teams to deploy models into production environments
• Contribute to versioning, validation, and monitoring strategies for AI systems
• Participate in debugging, root cause analysis, and performance improvements
• Maintain clean, reproducible training pipelines and documentation
• Collaboration & Engineering Excellence – Work closely with product managers, data engineers, and backend teams in an Agile environment
• Maintain clean code, well-documented experiments and structured project plans
• Contribute to shared AI best practices and reusable components
• Clearly communicate technical findings to both engineering and business stakeholders

Requirements

• Strong experience in machine learning model development and evaluation
• Hands‑on experience with TensorFlow / PyTorch
• Solid Python programming skills (Pandas, NumPy, Scikit-learn, SciPy, etc.)
• Experience working with structured and unstructured datasets
• Understanding of NLP concepts and transformer-based architectures
• Experience building production‑quality software in Collaborative environments
• Ability to run iterative development cycles and deliver measurable outcomes
• Nice to Have – Exposure to LLMs, embeddings, and RAG-based systems
• Familiarity with MLOps concepts (model versioning, CI/CD, monitoring)
• Experience deploying AI services on AWS
• Experience with experiment tracking tools (MLflow or similar)
• Experience working with SQL and relational databases