Senior Associate – AI ML Engineer

Riveron

AI/ML Engineer building and deploying Generative AI applications for Riveron, a finance consulting firm. Developing RAG, agentic workflows, APIs, evaluations, and cloud production services.

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

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining AI/ML applications, with a strong focus on Generative AI, data pipelines, and model workflows. Proficient in Python programming, REST APIs, and cloud services, while adhering to secure AI development practices.

Highest-signal resume keywords

Python ProgrammingGenerative AI DevelopmentREST API IntegrationAWS AI/ML CertificationCI/CD Workflows

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data PipelinesModel WorkflowsPrompt EngineeringEvaluation PipelinesContainerizationAsynchronous ProcessingObject-Oriented DesignUnit TestingIntegration TestingError Handling

Soft Skills

Analytical SkillsCommunication SkillsCollaboration Skills

Tools & Technologies

GitGitHubDockerKubernetesAWSAzureGCPPandasNumPyScikit-learn

Certifications & Qualifications

AWS AI/ML CertificationMicrosoft Azure AI Certification

Industry Keywords

Generative AIMachine LearningRetrieval-Augmented GenerationVector DatabasesAgent WorkflowsResponsible AIObservabilityModel VersioningExperiment TrackingTechnical Documentation

Tech Stack

Tools & technologies

AWSAzureCloudDockerGoogle Cloud PlatformKubernetesNumpyPandasPythonPyTorchScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

Build and maintain end-to-end AI/ML and Generative AI applications, including data pipelines, model or prompt workflows, APIs, evaluation, deployment, and monitoring
Design Retrieval-Augmented Generation solutions using document ingestion, chunking, embeddings, vector search, reranking, citations, and access-aware retrieval
Develop agentic AI workflows with tools, structured outputs, state or memory, orchestration, guardrails, human-in-the-loop approvals, and failure recovery
Integrate foundation models and AI services from commercial and open-source ecosystems
Select models based on quality, latency, cost, privacy, and deployment constraints
Implement prompt engineering, few-shot patterns, function/tool calling, structured output validation, and justified fine-tuning or parameter-efficient tuning
Create reproducible evaluation pipelines and maintain regression or golden test datasets
Develop production services using Python, REST APIs, asynchronous processing, and well-defined interfaces
Use Git/GitHub for version control, pull requests, code review, issue tracking, and release management
Implement CI/CD workflows with GitHub Actions or equivalent tools
Containerize and deploy applications using Docker and cloud services
Contribute to Kubernetes-based deployments, autoscaling, secrets management, observability, and rollback strategies
Apply secure AI development practices, including privacy controls, prompt-injection defenses, authorization checks, secrets handling, content safety, auditability, and responsible AI principles
Collaborate with product managers, data scientists, software engineers, cloud/platform teams, and business stakeholders
Create technical documentation, architecture notes, runbooks, and knowledge-sharing materials
Participate in design reviews, code reviews, and agile delivery ceremonies

Requirements

What you’ll need

Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Engineering, or a related field—or equivalent practical experience
3–5 years of professional experience developing software, data, or machine learning solutions, including substantial hands-on experience with Generative AI or LLM-based applications
Strong Python programming skills
Practical experience with pandas, NumPy, scikit-learn, PyTorch, TensorFlow, or equivalent
Working knowledge of prompting, embeddings, RAG, vector databases, tool/function calling, structured outputs, and agent workflows
Experience building and consuming REST APIs, working with JSON and schemas, and integrating databases, enterprise systems, or external services
Understanding of object-oriented or modular design, unit and integration testing, logging, error handling, code review, documentation, and debugging
Hands-on experience with Git/GitHub and CI/CD concepts; ability to create or maintain automated build, test, security-scan, and deployment workflows
Experience with at least one cloud platform: AWS, Azure, or GCP
Experience with Docker; familiarity with Kubernetes is beneficial
A current, role-relevant AWS AI/ML certification or Microsoft Azure AI certification is mandatory
Understanding of ML/LLM evaluation, experiment tracking, model or prompt versioning, observability, and production monitoring
Strong analytical, communication, and collaboration skills
Preferred: experience with GenAI/agent frameworks or SDKs, vector stores/search platforms, LLMOps/MLOps tooling, SQL and data modeling, streaming/queues/workflow orchestration/distributed processing, enterprise AI use cases, responsible AI, open-source contributions, technical writing, hackathon projects, or a portfolio of deployed AI applications

Benefits

Comp & perks

Medical, dental, and vision insurance
401(k) with company match
PTO
Flexible work arrangements
Progressive benefits
Mentorship and growth opportunities
Meaningful opportunities for impactful work supporting well-being in and out of the office