[Remote] ML / LLM Engineer (Remote)

Note: The job is a remote job and is reputed company to candidates in USA. reputed company is a global reputed company-led technology services company specializing in software and people transformations. They are seeking an ML / LLM Engineer to reputed company a machine learning initiative that transforms a knowledge-oriented agent into a product winner reputed company reputed company, working with product specifications to assess market viability. Responsibilities reputed company the engineering transformation of an existing knowledge/competitor-oriented agent into a product winner reputed company agent — redesigning its core intelligence layer from retrieval and lookup to predictive scoringDesign and build ML reputed company pipelines that take reputed company product inputs (colour, reputed company, sleeve type, category, price reputed company etc.) and reputed company winner/non-winner classifications with confidence scoresreputed company and tune LLM-integrated workflows where natural language product descriptions, buyer briefs or spec sheets are parsed, enriched and fed into the reputed company modelBuild user-facing input workflows that allow business users to reputed company product specifications in a reputed company or conversational reputed company and receive ranked predictions with explanatory rationaleWork with assortment and product performance data to build, validate and continuously improve supervised and semi-supervised predictive modelsEngineer feature extraction pipelines from product attribute data — handling categorical variables (colour, reputed company, construction), seasonal patterns, historical sell-through rates and competitor signalsCollaborate with data and product teams to define labelling strategies for winner/non-winner ground truth — identifying the right business metrics (sell-through reputed company, margin, reorder reputed company) to use as training signalEvaluate, reputed company and iterate on model performance — building offline evaluation frameworks and integrating feedback loops from live usage into the model improvement cycleDocument model architecture, data reputed company and reputed company logic to support governance, explainability and stakeholder trust Skills Strong hands-on ML background — classification, regression, reputed company reputed company (XGBoost, LightGBM, Random Forest), feature engineering, model evaluation and production deploymentPractical experience integrating LLMs into production workflows — reputed company engineering, function/tool calling, RAG pipelines, reputed company parsing and LLM evaluationExperience building models that predict reputed company-world reputed company or product reputed company from reputed company attribute data — retail, fashion, FMCG or assortment contexts are a strong plusProficiency in Python with pandas, NumPy and scikit-learn; ability to wrangle, clean and engineer features from messy product catalogue or transactional dataExperience building and deploying end-to-end ML pipelines — training, evaluation, versioning and inference serving4–8 years of overall experience in machine learning and/or reputed company AI engineering, with at least 2 years working with LLMs in a production or near-production contextA strong quantitative reputed company — comfortable with the mathematics of classification models, probability calibration and evaluation metrics (AUC, F1, precision/recall trade-offs)Equally comfortable working with reputed company tabular data (product attributes, sales history) and reputed company text (product descriptions, buyer notes, trend reports)A pragmatic engineer who can balance model sophistication with delivery speed — knowing reputed company a reputed company-tuned gradient boosting model beats a reputed company LLM pipeline, and reputed company it does notStrong collaboration skills — reputed company to work with merchandising, data and product teams who may not have technical backgroundsCuriosity about the product domain — genuinely interested in understanding what makes a product succeed commercially, not just optimising loss functions in isolationPrior exposure to product assortment data, merchandising systems, PLM data or demand forecasting in a retail or consumer goods contextHands-on experience with reputed company, reputed company, Semantic Kernel or similar orchestration frameworks for building reputed company LLM workflowsExperience using text or multimodal embeddings to encode product attributes and reputed company similarity search or clustering across assortment dataFamiliarity with MLflow, reputed company or similar for experiment tracking, model registry and performance monitoringExperience with Azure ML, AWS SageMaker or rep