Senior AI/ML Engineer

Riveron

Senior AI/ML Engineer building production GenAI, RAG, agentic, and MCP solutions. Supporting Riveron’s finance consulting clients through cloud-native AI platforms and enterprise integrations.

Posted 8/22/2026full-timePune • 🇮🇳 IndiaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and deploying AI/ML solutions, with a strong focus on GenAI applications, data pipelines, and observability. Proficient in Python and cloud technologies, ensuring secure and efficient integration of enterprise tools and services.

Highest-signal resume keywords

Advanced PythonGenAI/LLM Application DevelopmentAWS or Azure Cloud DeploymentMLOps/LLMOps PracticesModel Context Protocol (MCP) Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data PipelinesModel WorkflowsAPIs DesignEvent-Driven WorkflowsContainerization with DockerKubernetesInfrastructure as CodePandasNumPyScikit-Learn

Soft Skills

MentoringCollaborationLeadership

Tools & Technologies

Git/GitHubCI/CDPyTorchTensorFlowCloud ServicesDocument RepositoriesERP/CRM IntegrationData LakesVector TechnologiesObservability Platforms

Certifications & Qualifications

AWS AI/ML CertificationMicrosoft Azure AI Certification

Industry Keywords

AI/ML SolutionsGenAIAgentic ArchitectureData GovernanceResponsible AIModel Risk ManagementRegulatory CompliancePrivacyQuality GatesHuman-in-the-Loop Controls

Tech Stack

Tools & technologies

AWSAzureCloudDockerERPKubernetesNumpyPandasPythonPyTorchScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

Own AI/ML and GenAI solutions end to end, including data pipelines, model and prompt workflows, APIs, evaluation, deployment, observability, and continuous optimization
Design enterprise RAG platforms with secure ingestion, chunking, embeddings, hybrid/vector search, reranking, citations, and access-aware retrieval
Build production agentic systems with tool/function calling, structured outputs, planning and memory, multi-agent orchestration, human-in-the-loop approvals, failure recovery, and auditable traces
Architect and operate MCP clients and servers exposing enterprise tools, resources, and prompts with secure transports, authentication, least-privilege access, tenant isolation, and protections against prompt injection and unsafe tool execution
Integrate agents with document repositories, source control, ticketing, databases, ERP/CRM, and cloud services through reusable connectors and governance patterns
Define evaluation strategies and quality gates for accuracy, groundedness, safety, latency, and cost
Establish end-to-end observability for agent and MCP activity
Build production services in Python with strong engineering practices, GitHub-based CI/CD, and cloud-native deployment on AWS or Azure using Docker, Kubernetes, and infrastructure as code
Partner with product, architecture, data science, security, and business stakeholders
Lead design and architecture reviews and mentor engineers

Requirements

What you’ll need

Bachelor's or Master's in Computer Science, Data Science, AI/ML, Engineering, or a related field — or equivalent practical experience
5–9 years developing production software, data, ML, or AI solutions, including hands-on delivery of GenAI/LLM applications
Advanced Python
Practical experience with pandas, NumPy, scikit-learn, PyTorch, TensorFlow, or equivalent
Strong grasp of LLM and agentic architecture, including prompting, context engineering, embeddings, RAG, tool/function calling, structured outputs, orchestration, evaluation, and human-in-the-loop controls
Experience designing APIs, distributed services, and event-driven or asynchronous workflows with secure tool execution for AI agents
Hands-on experience with Git/GitHub and CI/CD, including automated build, test, security-scan, and deployment workflows
Strong experience with AWS or Azure and containerized deployment using Docker
Kubernetes and infrastructure-as-code experience expected
Current, role-relevant AWS AI/ML certification or Microsoft Azure AI certification is mandatory
Hands-on production experience with Model Context Protocol (MCP) is mandatory
Experience with MLOps/LLMOps practices, including experiment tracking, model and prompt versioning, tracing, evaluation, monitoring, and cost optimization
Preferred: GenAI or agent frameworks, enterprise search and vector technologies, LLMOps/observability platforms, SQL and data modeling, streaming, workflow orchestration, data lakes/lakehouse platforms, enterprise AI domains, responsible AI, model risk management, data governance, privacy, regulatory/client compliance, and mentoring

Benefits

Comp & perks

Medical, dental, and vision insurance
401(k) with company match
PTO
Flexibility
Progressive benefits
Mentorship and growth opportunities
Well-being support