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Remote role plus mid-level ML/AI demand, but strict niche MCP and certification requirements reduce applicant pool.
Role requires specialized GenAI, MCP, and cloud MLOps expertise, limiting cross-industry transferability.
Multiple mandatory filters: explicit years, AWS/Azure AI certification, and hands-on MCP experience make screening strict.
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Own end-to-end design, build, deployment, and optimization of AI/ML and Generative AI enterprise solutions including data pipelines, model workflows, APIs, and evaluation.
Architect and operate secure enterprise RAG platforms and agentic systems with integration to enterprise tools and governance.
Lead technical architecture reviews, mentor engineers, and collaborate with cross-functional stakeholders for complex AI solution delivery.
5–9 years of experience building production AI/ML and Generative AI/LLM applications.
Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, Engineering, or related field, or equivalent experience.
Mandatory hands-on experience with Python, AI/ML libraries (pandas, NumPy, TensorFlow, PyTorch, etc.), and cloud deployments on AWS or Azure including Docker and Kubernetes.
Must hold a current, role-relevant AWS AI/ML or Microsoft Azure AI certification and have hands-on production experience with the Model Context Protocol (MCP).
Deep expertise in LLMs and agentic AI architectures: prompting, embeddings, RAG, tool/function calling, orchestration, and human-in-the-loop controls.
Experienced in designing secure, distributed API services with CI/CD and infrastructure-as-code in enterprise cloud environments.
Proven ability to lead and deliver complex AI solutions in enterprise domains with strong architectural judgment and mentoring skills.