Responsibilities
- Deploy and integrate pre-trained and fine-tuned ML / LLM models using OpenAI, Hugging Face, TensorFlow, PyTorch, or similar frameworks
- Build scalable AI inference APIs using FastAPI, Flask, Node.js, or similar technologies
- Implement retrieval-augmented generation (RAG) pipelines using vector databases such as Pinecone, Weaviate, Chroma, or FAISS
- Optimize prompt engineering, embeddings, and AI workflows for performance, accuracy, and cost efficiency
- Build responsive front-end applications using React, Next.js, Vue, or similar frameworks
- Develop back-end services and APIs connecting AI systems to business workflows and user-facing applications
- Design scalable architectures for chatbots, AI assistants, analytics dashboards, search systems, and workflow automation tools
- Ensure applications are intuitive, secure, responsive, and production-ready
- Build ETL/ELT pipelines for ingesting, cleaning, transforming, and processing structured and unstructured datasets
- Automate data preprocessing, versioning, labeling, and pipeline orchestration using Airflow, Prefect, Dagster, or similar tools
- Store and manage datasets within cloud warehouses such as Snowflake, BigQuery, or Redshift
- Maintain reliable data flows supporting training, inference, analytics, and AI operations
- Containerize AI services using Docker and deploy workloads to Kubernetes or cloud-native environments
- Build and maintain CI/CD pipelines for AI model updates and application releases
- Monitor inference latency, application performance, costs, and model drift using MLflow, Weights & Biases, Prometheus, or custom dashboards
- Support scalable and reliable cloud infrastructure on AWS, GCP, or Azure
- Ensure AI systems comply with GDPR, HIPAA, SOC 2, or relevant privacy/security standards
- Implement authentication, access control, rate limiting, and secure API practices
- Protect user data and AI workflows using modern security standards and best practices
- Collaborate with product managers, designers, and data scientists to prioritize impactful AI features
- Translate prototypes into production-grade systems with scalable architecture and maintainable code
- Participate in sprint planning, architecture discussions, code reviews, and technical documentation
- Maintain clear documentation to support reproducibility, onboarding, and long-term maintainability
Requirements
- 3+ years of professional software engineering experience with AI/ML exposure
- Strong proficiency in Python and JavaScript/TypeScript
- Experience with AI/ML frameworks such as PyTorch, TensorFlow, LangChain, or Hugging Face
- Experience deploying AI or ML models into production systems
- Strong front-end experience with React, Next.js, or Vue
- Strong SQL skills and experience with cloud data warehouses
- Familiarity with REST APIs, microservices, and distributed systems
- Experience with Docker, CI/CD workflows, and cloud infrastructure
Nice to Have
- Experience building and scaling AI-powered SaaS applications
- Strong understanding of embeddings, vector databases, and RAG architectures
- Experience with LLM fine-tuning, evaluation, and prompt optimization
- Familiarity with MLOps tools such as MLflow, Kubeflow, Vertex AI, SageMaker, or Weights & Biases
- Experience with serverless architectures and cost-optimized inference systems
- Background in SaaS, automation platforms, analytics systems, or AI-driven products
Additional Information
- Working Hours: U.S. client business hours (with flexibility for deployments, experimentation cycles, and sprint schedules)
