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Note: The job is a remote job and is open to candidates in USA. Codvo.ai is a global empathy-led technology services company specializing in software and people transformations. They are seeking an ML / LLM Engineer to lead a machine learning initiative that transforms a knowledge-oriented agent into a product winner prediction engine, working with product specifications to assess market viability.
Responsibilities
Lead the engineering transformation of an existing knowledge/competitor-oriented agent into a product winner prediction agent — redesigning its core intelligence layer from retrieval and lookup to predictive scoring
Design and build ML prediction pipelines that take structured product inputs (colour, fabric, sleeve type, category, price point etc.) and output winner/non-winner classifications with confidence scores
Develop and tune LLM-integrated workflows where natural language product descriptions, buyer briefs or spec sheets are parsed, enriched and fed into the prediction model
Build user-facing input workflows that allow business users to enter product specifications in a structured or conversational interface and receive ranked predictions with explanatory rationale
Work with assortment and product performance data to build, validate and continuously improve supervised and semi-supervised predictive models
Engineer feature extraction pipelines from product attribute data — handling categorical variables (colour, fabric, construction), seasonal patterns, historical sell-through rates and competitor signals
Collaborate with data and product teams to define labelling strategies for winner/non-winner ground truth — identifying the right business metrics (sell-through rate, margin, reorder rate) to use as training signal
Evaluate, benchmark and iterate on model performance — building offline evaluation frameworks and integrating feedback loops from live usage into the model improvement cycle
Document model architecture, data lineage and prediction logic to support governance, explainability and stakeholder trust
Skills
Strong hands-on ML background — classification, regression, ensemble methods (XGBoost, LightGBM, Random Forest), feature engineering, model evaluation and production deployment
Practical experience integrating LLMs into production workflows — prompt engineering, function/tool calling, RAG pipelines, output parsing and LLM evaluation
Experience building models that predict real-world commercial or product outcomes from structured attribute data — retail, fashion, FMCG or assortment contexts are a strong plus
Proficiency in Python with pandas, NumPy and scikit-learn; ability to wrangle, clean and engineer features from messy product catalogue or transactional data
Experience building and deploying end-to-end ML pipelines — training, evaluation, versioning and inference serving
4–8 years of overall experience in machine learning and/or applied AI engineering, with at least 2 years working with LLMs in a production or near-production context
A strong quantitative foundation — comfortable with the mathematics of classification models, probability calibration and evaluation metrics (AUC, F1, precision/recall trade-offs)
Equally comfortable working with structured tabular data (product attributes, sales history) and unstructured text (product descriptions, buyer notes, trend reports)
A pragmatic engineer who can balance model sophistication with delivery speed — knowing when a well-tuned gradient boosting model beats a complex LLM pipeline, and when it does not
Strong collaboration skills — able to work with merchandising, data and product teams who may not have technical backgrounds
Curiosity about the product domain — genuinely interested in understanding what makes a product succeed commercially, not just optimising loss functions in isolation
Prior exposure to product assortment data, merchandising systems, PLM data or demand forecasting in a retail or consumer goods context
Hands-on experience with LangChain, LlamaIndex, Semantic Kernel or similar orchestration frameworks for building agentic LLM workflows
Experience using text or multimodal embeddings to encode product attributes and perform similarity search or clustering across assortment data
Familiarity with MLflow, Weights & Biases or similar for experiment tracking, model registry and performance monitoring
Experience with Azure ML, AWS SageMaker or Google Vertex AI for scalable training and deployment
Experience building agentic pipelines where LLMs orchestrate multiple tool calls, data lookups and model inference steps in sequence
Company Overview
At Codvo.ai, we help enterprises move beyond AI experimentation to real AI execution. It was founded in 2019, and is headquartered in Plano, Texas, USA, with a workforce of 51-200 employees. Its website is
Company H1B Sponsorship
Codvo.ai has a track record of offering H1B sponsorships, with 2 in 2025, 3 in 2022. Please note that this does not guarantee sponsorship for this specific role.
