Software Engineer - AI/ML

Аналитика рынка

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Описание

We are looking for a Data Scientist – ML & Generative AI with strong hands-on experience across traditional Machine Learning, Deep Learning, Generative AI, and Computer Vision . The ideal candidate will be able to translate business problems into scalable AI/ML solutions and take models from experimentation through production deployment. The role will have a particular focus on building AI solutions for Supply Chain, Operations, Forecasting, Optimization, and Vision-based use cases . 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 PyTorch and/or TensorFlow . 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 Large Language Models (LLMs) . 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 Programming Strong Python programming skills SQL and data manipulation Pandas, NumPy, Scikit-learn Machine Learning Scikit-learn XGBoost / LightGBM or equivalent Statistical modeling Time-series forecasting Feature engineering and model evaluation Deep Learning PyTorch and/or TensorFlow Transformers CNNs and modern neural-network architectures Generative AI LLMs and foundation models RAG Prompt engineering Embeddings Vector databases LLM evaluation Agentic AI concepts Frameworks such as LangChain, LlamaIndex, Semantic Kernel, or equivalent Computer Vision OpenCV Object detection / segmentation models CNNs / Vision Transformers OCR and image-processing techniques Data & Cloud Experience working with large datasets and cloud-based data platforms. Exposure to AWS, Azure, or GCP . Familiarity with Databricks, Spark, Snowflake, or similar platforms is an advantage. Требования: Qualifications & Experience Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, Artificial Intelligence, Statistics, Mathematics, Engineering , or a related quantitative discipline. Approximately 4–7 years of relevant industry experience in Data Science, Machine Learning, or AI. Strong hands-on experience developing end-to-end ML solutions. Practical experience with at least one of Generative AI, Computer Vision, or advanced Deep Learning , 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 statistical methods, traditional ML, deep learning, computer vision, and GenAI based on the business problem. Ability to communicate complex analytical concepts to both technical and non-technical stak

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