Note: The job is a remote job and is open to candidates in USA. INSPYR Solutions provides technology and talent solutions aligned with client business objectives. The Senior AI Engineer will lead the design, development, and deployment of enterprise-wide AI systems and intelligent agents, while establishing architecture standards and integrating AI capabilities into business applications. The role also collaborates with cross-functional teams, operationalizes machine learning models, mentors engineers, and communicates AI strategy and results.
Responsibilities
- Design, build, and deploy end-to-end AI systems — from data ingestion and model development through inference, monitoring, and continuous improvement
- Architect and develop AI agents and multi-agent frameworks capable of reasoning, planning, and executing complex workflows autonomously
- Build cohesive AI solutions through the orchestration and integration of Models, LLMs, agentic services, expert systems, and knowledge graphs
- Leverage AI coding assistants (Claude Code, GitHub Codex, and similar tools) to accelerate development, automate repetitive engineering tasks, and improve code quality across the team
- Build and maintain scalable AI pipelines on Databricks and AWS, integrating with existing data infrastructure and enterprise systems
- Define and implement enterprise AI architecture standards, patterns, and best practices across the organization
- Evaluate and integrate large language models (LLMs), foundation models, and generative AI capabilities into business applications
- Collaborate with Data Scientists to operationalize ML models and move experiments from prototype to production
- Partner with cross-functional teams across multiple simultaneous initiatives to scope, design, and deliver AI-powered solutions
- Establish model monitoring, evaluation, and feedback loops to ensure AI systems remain accurate, safe, and performant in production
- Stay current with the rapidly evolving AI landscape and proactively recommend new tools, frameworks, and approaches that improve outcomes
- Mentor junior engineers and contribute to a culture of technical excellence, experimentation, and continuous learning
- Prepare technical documentation, architecture diagrams, and executive presentations to communicate AI strategy and results
Skills
- Holders or Authorized to Work in the U.S
- Bachelor''s degree in Computer Science, Engineering, Mathematics, or related field
- 7+ years of experience in software or data engineering with at least 5 years focused on AI/ML systems development
- Demonstrated end-to-end experience building and deploying AI systems and AI agents in production environments
- Proficiency with AI coding assistants such as Claude Code, GitHub Codex, or equivalent tools as part of an active development workflow
- Hands-on experience with Databricks for model training, feature engineering, and pipeline orchestration
- Solid experience with AWS cloud services (SageMaker, Lambda, S3, EC2, Step Functions, or equivalent) for AI/ML workloads
- Strong Python skills including SparkSQL, MLlib, PyTorch, spaCy, and NLTK for NLP and ML model development
- Experience integrating AI systems via REST APIs, GraphQL, and OAuth for secure, scalable enterprise connectivity
- Proven ability to operate as a senior independent contributor across multiple initiatives simultaneously without close supervision
- Experience designing enterprise AI architecture including APIs, orchestration layers, vector databases, and model serving infrastructure
- Master''s degree
- Experience building multi-agent systems and knowledge graphs using frameworks such as LangGraph, AutoGen, CrewAI, or the Anthropic Agent SDK
- Familiarity with front-end and visualization technologies including React/Native, Figma, Dash or similar, and Bootstrap for building AI-powered user interfaces and data applications
- Familiarity with prompt engineering, retrieval-augmented generation (RAG), and fine-tuning techniques for production LLM applications
- Experience with MLOps practices including CI/CD for AI systems, model versioning, and automated evaluation pipelines
- Knowledge of vector databases such as Pinecone, Weaviate, or pgvector for semantic search and retrieval applications
- Familiarity with data governance, AI safety, and responsible AI principles in enterprise settings
- Experience with Databricks Unity Catalog, Delta Lake, and MLflow for end-to-end model lifecycle management
- Strong communication and stakeholder management skills — able to present technical AI concepts clearly to both engineering teams and business executives
- Ability to evaluate build vs. buy tradeoffs for AI tooling and make architecture recommendations with long-term maintainability in mind
- Experience contributing to AI strategy, roadmap planning, and organizational AI adoption initiatives
- Attention to detail with a strong bias toward shipping reliable, well-documented, production-grade systems
Company Overview