As an AI Engineer at OmnifiCX, you will design, build, and deploy intelligent AI\-driven capabilities that power our commerce platform. You will work at the intersection of machine learning, backend engineering, and product intelligence — delivering features like smart order routing, predictive analytics, conversational AI assistants, and automation pipelines. You will collaborate closely with product, engineering, and data teams to translate business problems into production\-grade AI solutions.
### **Roles \& Responsibilities:**
#### **AI/ML Model Development:**
Design, train, and deploy machine learning models for commerce use cases such as order routing optimization, demand forecasting, fraud detection, and customer intent prediction.
Build and maintain end\-to\-end ML pipelines covering data ingestion, feature engineering, model training, evaluation, and serving.
Experiment with state\-of\-the\-art approaches including LLMs, transformers, and classical ML algorithms depending on the problem context.
Integrate large language models (e.g., OpenAI GPT, Anthropic Claude, open\-source models via Hugging Face) into OmnifiCX product workflows such as intelligent order assistants, auto\-summarization, and natural language query interfaces.
Design prompt engineering strategies, RAG (Retrieval\-Augmented Generation) pipelines, and agentic workflows for commerce\-specific scenarios.
Evaluate and benchmark LLM outputs for accuracy, latency, cost, and safety before production rollout.
#### **AI\-Powered Order Routing \& Optimization:**
Collaborate with the OMS product team to embed AI into the order routing engine — building models that optimise routing decisions based on inventory, SLAs, cost, carrier performance, and real\-time signals.
Develop rule\-augmented ML models that work alongside deterministic business logic in the routing module.
Monitor model performance in production and implement feedback loops for continuous improvement.
#### **Data Engineering \& Feature Pipelines:**
Build and maintain data pipelines for structured and unstructured commerce data
Work with data and platform teams to define feature stores, data schemas, and batch/streaming data flows for model training and inference.
Ensure data quality, lineage, and reproducibility across ML experiments.
#### **Collaboration \& Mentorship:**
Work closely with product managers, backend engineers, and business analysts to scope and deliver AI features aligned with OmnifiCX roadmap priorities.
Mentor junior engineers on AI/ML best practices and promote a culture of data\-driven decision making.
Document AI system designs, model cards, and experiment outcomes for cross\-functional transparency.
### **Experience and Skills:**
#### **Minimum:**
Experience: 4–8 years in AI/ML engineering, with at least 2 years delivering production ML systems.
Machine Learning: Hands\-on experience with supervised, unsupervised, and reinforcement learning. Proficiency in scikit\-learn, XGBoost, LightGBM, and deep learning frameworks (PyTorch or TensorFlow).
LLMs \& Generative AI: Practical experience integrating LLM APIs (OpenAI, Anthropic, Cohere, or open\-source). Familiar with LangChain, LlamaIndex, prompt engineering, and RAG pipeline design.
Programming: Strong Python skills. Proficiency in Pandas, NumPy, and ML experimentation tooling.
Data Engineering: Experience building pipelines using Apache Spark, Airflow, or dbt. Comfortable with SQL and large structured datasets.
Model Serving \& APIs: Experience deploying ML models as REST/gRPC microservices using FastAPI or Flask.
MLOps: Experience with model monitoring, versioning, and CI/CD for ML pipelines.
Cloud Platforms: Hands\-on experience with AWS or GCP/Azure equivalents.
Optimization Problems: Ability to frame business problems as optimization or ranking tasks.
Collaboration Tools: Familiarity with Jira, Confluence, or similar tools.
#### **Preferred:**
Certifications: AWS Certified Machine Learning – Specialty, Google Professional ML Engineer, or equivalent.
Commerce / OMS Domain: Exposure to e\-commerce, order management, supply chain, or logistics AI use cases.
Vector Databases: Experience with Pinecone, Weaviate, Chroma, or pgvector.