14 days ago
Pune, India or Bengaluru, IndiaMid Level / Senior
Responsibilities
- Build and maintain end-to-end AI/ML and Generative AI applications, including data pipelines, model and prompt workflows, APIs, evaluation, deployment, and monitoring.
- Design Retrieval-Augmented Generation solutions involving document ingestion, chunking, embeddings, vector search, reranking, citations, and access-aware retrieval.
- Develop agentic AI workflows with tools, structured outputs, memory, orchestration, guardrails, human-in-the-loop approvals, and failure recovery.
- Integrate commercial and open-source foundation models and AI services based on quality, latency, cost, privacy, and deployment constraints.
- Implement prompt engineering, few-shot patterns, function and tool calling, structured output validation, and justified fine-tuning or parameter-efficient tuning.
- Create evaluation pipelines and regression or golden datasets covering accuracy, relevance, groundedness, safety, latency, reliability, and cost.
- Develop clean, modular, documented, and testable Python production services with asynchronous processing and defined interfaces.
- Use Git and GitHub for version control, pull requests, code review, issue tracking, and release management, and maintain CI/CD workflows.
- Containerize and deploy applications using Docker and cloud services, contributing to Kubernetes deployments, autoscaling, secrets management, observability, and rollback strategies.
- Apply secure and responsible AI practices, including privacy controls, prompt-injection defenses, authorization, secrets handling, content safety, auditability, and governance.
- Collaborate with product, data science, software engineering, cloud/platform, and business stakeholders to deliver measurable technical outcomes.
- Create architecture notes, technical documentation, runbooks, and knowledge-sharing materials, and participate in design reviews, code reviews, and delivery ceremonies.
Requirements
- 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 Generative AI or LLM application experience.
- Strong Python skills and practical experience with data and ML libraries such as pandas, NumPy, scikit-learn, PyTorch, or TensorFlow.
- Working knowledge of prompting, embeddings, RAG, vector databases, tool and function calling, structured outputs, and agent workflows.
- Experience building and consuming APIs, working with JSON and schemas, and integrating databases, enterprise systems, or external services.
- Understanding of modular software design, unit and integration testing, logging, error handling, code review, documentation, and debugging.
- Hands-on Git/GitHub and CI/CD experience, including automated build, test, security-scan, and deployment workflows.
- Experience with AWS, Azure, or GCP and Docker; Kubernetes familiarity 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, including the ability to explain technical trade-offs to technical and non-technical audiences.
- Preferred qualifications include experience with GenAI or agent frameworks, vector stores, LLMOps/MLOps tooling, SQL and data modeling, streaming or workflow orchestration, enterprise AI use cases, responsible AI, data governance, regulatory compliance, open-source contributions, technical writing, hackathons, or a portfolio of deployed AI applications.
Benefits
- Full-time roles include medical, dental, and vision insurance, a 401(k) with company match, and paid time off.
- The company describes a flexible, inclusive, collaborative environment with mentorship, progressive benefits, and well-being support.
Tech Stack
AWSAzureDockerElasticsearchGitGitHub ActionsGoogle Cloud PlatformKubernetesMLflowNumPyPandasPythonPyTorchscikit-learnSQLTensorFlow
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