About the role
Job Title: Full-Stack AI Engineer
Position Type: Full-Time, Remote
Working Hours: U.S. client business hours (with flexibility for deployments, experimentation cycles, and sprint schedules)
About the Role
Our client is seeking a highly skilled Full-Stack AI Engineer to design, build, and deploy scalable AI-powered applications that solve real-world business problems.
This role bridges software engineering with applied machine learning, combining front-end development, back-end systems, AI model integration, and cloud infrastructure into production-ready applications. You will work across the full product lifecycle — from experimentation and prototyping to deployment, optimization, and monitoring.
The ideal candidate is both technically strong and execution-focused, capable of building AI-driven systems that are scalable, reliable, performant, and user-friendly.
Responsibilities
AI Model Integration & LLM Systems
- 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
Full-Stack Application Development
- 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
Data Engineering & Pipeline Development
- 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
Infrastructure, Deployment & MLOps
- 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
Security & Compliance
- 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
Collaboration & Product Development
- 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
What Makes You a Perfect Fit
- Strong software engineer with deep curiosity around AI/ML systems and emerging technologies
• Comfortable moving quickly from prototype to production-grade deployment
• Analytical and solutions-oriented with strong debugging and optimization skills
• Able to balance performance, scalability, usability, and operational cost
• Collaborative communicator who works effectively across technical and non-technical teams
Required Experience & Skills
- 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
Preferred Experience & Skills
- 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
What Does a Typical Day Look Like?
A Full-Stack AI Engineer’s day revolves around transforming AI capabilities into scalable, production-ready applications. You will:
- Review and optimize AI model APIs for latency, accuracy, and reliability
• Build front-end interfaces that expose AI-driven functionality to end users
• Maintain and improve data pipelines supporting AI systems and analytics
• Deploy updates through CI/CD workflows and monitor production performance
• Collaborate with product and data science teams on AI feature prioritization
• Debug infrastructure, inference, or workflow issues impacting system performance
• Document architectures, workflows, and deployment processes for maintainability and scaling
In essence: you ensure AI systems move beyond prototypes into secure, scalable, reliable, and impactful production applications.
Key Metrics for Success (KPIs)
- Successful deployment of AI features aligned with sprint timelines
• Application uptime ≥ 99.9%
- Inference latency maintained below target thresholds
• Reduction in manual workflows through AI automation
• Stable model performance and minimized drift or degradation
• Positive adoption and engagement with AI-powered features
• Scalable, maintainable, and cost-efficient AI infrastructure
Interview Process
- Initial Phone Screen
• Video Interview with Pavago Recruiter
- Technical Assessment (e.g., deploy an ML model with API + front-end integration)
• Client Interview(s) with Engineering / Product Teams
• Offer & Background Verification
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Originally posted on Himalayas