Software Engineer - AI/ML

. The candidate should be comfortable working with structured, unstructured, image, and text data and collaborating with business, engineering, product, and data teams.

Key Responsibilities

Machine Learning & Predictive Analytics

• Design, develop, and evaluate machine learning models for classification, regression, forecasting, clustering, recommendation, anomaly detection, and optimization problems.
• Apply statistical and machine learning techniques such as Linear/Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, XGBoost/LightGBM, clustering, and time-series modeling.
• Perform feature engineering, feature selection, model tuning, validation, and performance analysis.
• Build reusable and scalable ML pipelines for real-world business applications.

Deep Learning

• Develop deep learning solutions using frameworks such as

.

• Work with neural network architectures including CNNs, RNN/LSTMs, Transformers, and other modern deep learning architectures.
• Evaluate and optimize deep learning models for accuracy, latency, and scalability.

Generative AI & LLMs

• Design and develop GenAI applications leveraging

.

• Build solutions using techniques such as: • Prompt engineering
• Retrieval-Augmented Generation (RAG)
• Embeddings and vector search
• LLM orchestration
• Function/tool calling
• AI agents and multi-step workflows
• Fine-tuning / parameter-efficient fine-tuning where appropriate

• Work with structured and unstructured enterprise data to create domain-specific GenAI applications.
• Develop evaluation frameworks for LLM applications covering response quality, hallucination, groundedness, relevance, latency, and cost.
• Implement appropriate guardrails and responsible AI practices for production GenAI solutions.

Computer Vision

• Develop and deploy computer vision solutions for use cases such as: • Image classification
• Object detection
• Image segmentation
• OCR and document/image understanding
• Visual inspection and defect detection
• Product/image recognition

• Work with modern computer vision architectures and pretrained/foundation models.
• Experience with OpenCV, YOLO, CNNs, Vision Transformers, or multimodal models is desirable.

Supply Chain & Operations Analytics

Develop AI/ML solutions addressing supply-chain and operational problems such as:

• Demand forecasting
• Sales forecasting
• Inventory optimization
• Stock-out / overstock prediction
• Replenishment recommendations
• Lead-time prediction
• Supply and demand imbalance detection
• Logistics and transportation analytics
• ETA prediction
• Warehouse analytics
• Supplier performance and risk analytics
• Product allocation and assortment optimization
• Anomaly detection
• Scenario planning and decision-support solutions

The candidate should be able to work closely with supply-chain stakeholders to convert business requirements into analytical and AI/ML solutions.

Model Deployment & MLOps

• Collaborate with engineering teams to deploy ML and GenAI solutions into production.
• Develop APIs, batch pipelines, or real-time inference services for model consumption.
• Apply good software engineering practices including modular development, version control, testing, documentation, and code reviews.
• Understand concepts such as model monitoring, drift detection, model versioning, experimentation, and retraining.
• Experience with Docker, CI/CD, MLflow, Kubernetes, or similar MLOps technologies is desirable.

Required Technical Skills

• Strong Python programming skills
• SQL and data manipulation
• Pandas, NumPy, Scikit-learn

• Scikit-learn
• XGBoost / LightGBM or equivalent
• Statistical modeling
• Time-series forecasting
• Feature engineering and model evaluation

• PyTorch and/or TensorFlow
• Transformers
• CNNs and modern neural-network architectures

• LLMs and foundation models
• RAG
• Prompt engineering
• Embeddings
• Vector databases
• LLM evaluation
• Agentic AI concepts
• Frameworks such as LangChain, LlamaIndex, Semantic Kernel, or equivalent

• OpenCV
• Object detection / segmentation models
• CNNs / Vision Transformers
• OCR and image-processing techniques

• Experience working with large datasets and cloud-based data platforms.
• Exposure to

.

• Familiarity with Databricks, Spark, Snowflake, or similar platforms is an advantage.

Qualifications & Experience

• Bachelor's or Master's degree in

, or a related quantitative discipline.

• Approximately

in Data Science, Machine Learning, or AI.

• Strong hands-on experience developing end-to-end ML solutions.
• Practical experience with at least one of

, with willingness and ability to work across all areas.

• Experience solving supply-chain, retail, manufacturing, logistics, or operations-related problems is strongly preferred.
• Experience taking ML models beyond proof-of-concept into production or business adoption.

What We Are Looking For

The successful candidate will demonstrate:

• Strong problem-solving and analytical thinking.
• Solid understanding of ML fundamentals rather than reliance solely on pre-built GenAI APIs.
• Ability to select the right approach across

based on the business problem.

• Ability to communicate complex analytical concepts to both technical and non-technical stakeholders.
• Strong experimentation mindset with an ability to measure business and model impact.
• Curiosity to continuously learn and apply emerging AI technologies.
• Ability to independently own moderately complex projects while collaborating effectively with senior data scientists, engineers, product teams, and business stakeholders.

Preferred / Good-to-Have Experience

• Experience with

.

• Experience building production-grade RAG or enterprise GenAI applications.
• Exposure to multimodal AI combining text, image, and structured data.
• Experience with optimization / operations research techniques.
• Experience with forecasting at scale across products, locations, or customers.
• Knowledge of responsible AI, explainability, privacy, and AI governance.
• Experience working in Agile product or AI development teams.