Ai Ml Engineer
- EXL
- 5 - 7 years
- Noida, Gurugram
- 4 days ago
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Job Description
AI/ML Engineer - Forecasting & Optimization
Position Overview
We are seeking a Senior AI/ML Engineer to develop and deploy advanced forecasting models and AI-driven features for our Financial Forecasting Platform. You will design and implement closed-loop learning systems that continuously improve forecast accuracy, detect anomalies in real time, and provide intelligent decision support through LLM-powered agents. Your models will directly impact demand planning, margin optimization, and working capital across thousands of product families.
Key Responsibilities
Forecasting Model Development
- Design, train, and deploy advanced demand forecasting models targeting 3-4% MAPE/WAPE accuracy (baseline: 6%)
- MAPE = Mean Absolute Percentage Error; WAPE = Weighted Absolute Percentage Error
- Build models at granular levels (SKU, product family) with scenario analysis capabilities
- Implement bias detection and correction for over/under forecast scenarios
- Create models that detect price vs. volume trends and mix changes in real time
- Develop methods to assess forecast assumption validity and validate assumptions vs. actuals
- Compare forecast vs. quote pricing and identify gaps for proactive calibration
Closed-Loop AI Learning & Continuous Calibration
- Build systems that automatically test assumptions vs. actuals each forecast cycle
- Implement recalibration logic to improve model performance over time
- Develop feedback loops to detect when assumptions break and trigger retraining
- Create model monitoring and performance tracking systems
- Implement A/B testing frameworks to validate model improvements
- Design systems that surface model drift and trigger intervention
Real-Time Anomaly Detection
- Develop algorithms to detect anomalies in pricing gaps, derates, and forecast deviations
- Flag issues such as "price below quote" and overly conservative assumptions
- Create early warning systems to surface problems during planning (not post-mortem analysis)
- Implement explainability features to help users understand detected anomalies
- Design recommendation engines for proactive forecast calibration adjustments
LLM-Powered Insights & Prompt Engineering
- Develop prompt engineering strategies for LLM-based financial analysis agents
- Build conversational interfaces for complex, multi-source financial questions
- Create dynamic, on-demand insight generation capabilities using AI agents
- Implement RAG (Retrieval-Augmented Generation) for grounding insights in forecast data
- Design systems allowing users to refine and drill into insights through follow-up prompts
Model Deployment & Operations
- Deploy models as production APIs serving sub-second latency requirements
- Build model versioning and governance systems
- Implement automated retraining pipelines
- Create documentation and usage guides for model limitations and performance
- Support cross-functional teams with result interpretability
Required Qualifications
Education
- Bachelor's degree in Computer Science, Engineering, Statistics, Mathematics, or related field
- Master's degree in Machine Learning, Data Science, or Operations Research preferred
Experience
- 7+ years of machine learning development experience
- 4+ years building production forecasting or time-series prediction models
- 2+ years working with LLMs and prompt engineering
- Proven experience improving forecast accuracy metrics (MAPE, WAPE, bias reduction)
- Experience deploying models in enterprise environments
Technical Skills
Programming & ML Frameworks:
- Python (primary), R, SQL
- Scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch
- ARIMA, Prophet, Exponential Smoothing, LSTM, Transformer-based models
LLM & AI:
- Experience with GPT, Claude, LLaMA
- Prompt design and RAG (Retrieval-Augmented Generation) architectures
- Vector databases (Pinecone, FAISS, Weaviate, Milvus)
Model Deployment:
- MLflow, Weights & Biases, Kubeflow, SageMaker
- API serving frameworks (FastAPI, Flask)
- Cloud platforms (AWS SageMaker, Azure ML, GCP Vertex AI)
Data Processing:
- Pandas, PySpark, SQL
- Feature engineering at scale
- Data validation and quality assurance
Domain Knowledge
- Strong understanding of demand planning and financial forecasting
- Knowledge of manufacturing cost structures and pricing dynamics
- Familiarity with working capital and inventory management implications
- Understanding of bias, seasonality, and mix changes in forecasting
Soft Skills
- Strong analytical and problem-solving abilities
- Excellent communicationability to explain model performance to non-technical stakeholders
- Self-motivated with ability to work independently
- Strategic thinking combined with execution excellence
- Continuous learning mindset
Preferred Qualifications
- Experience with causal inference and assumption testing in forecasting
- Knowledge of optimization techniques and constraint satisfaction
- Familiarity with financial close processes and variance analysis
- Experience building explainable AI systems for business users
- Publication or patents in forecasting or anomaly detection
- Cloud ML platform certifications
- Experience with Bayesian methods and uncertainty quantification
Job Classification
Industry: Analytics / KPO / ResearchFunctional Area / Department: Data Science & AnalyticsRole Category: Data Science & Machine LearningRole: Machine Learning EngineerEmployement Type: Full timeContact Details:
Company: EXLLocation(s): Noida, Gurugram
Keyskills: Model deployment Artificial Intelligence Machine Learning Forecasting Large Language Model sagemaker Demand Forecasting ML flow Model Development Anomaly Detection GCP AI agent RAG Retrieval Augmented Generation AWS
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