INDStaff Software Engineer - AI

MLOps & AI Infrastructure

AI-Augmented Development

Technical Documentation & Mentorship

Required Qualifications

• Experience: 8+ years of professional software engineering experience, with 2+ years focused on AI/ML solution development and delivery
• Education: Bachelor's degree in Computer Science, Software Engineering, AI/ML, or related field (or equivalent experience)
• AI/ML Expertise: Strong understanding of large language model architectures, capabilities, and limitations Proven track record building and deploying production AI systems (RAG, agents, fine-tuning, embeddings, vector search) Proficiency with AI orchestration frameworks (LangChain, Semantic Kernel, AutoGen, CrewAI, LlamaIndex) Hands-on experience with major LLM providers and platforms (OpenAI, Anthropic, Google Vertex AI, AWS Bedrock) Solid understanding of prompt engineering, evaluation methodologies, and AI safety/guardrails
• Strong understanding of large language model architectures, capabilities, and limitations
• Proven track record building and deploying production AI systems (RAG, agents, fine-tuning, embeddings, vector search)
• Proficiency with AI orchestration frameworks (LangChain, Semantic Kernel, AutoGen, CrewAI, LlamaIndex)
• Hands-on experience with major LLM providers and platforms (OpenAI, Anthropic, Google Vertex AI, AWS Bedrock)
• Solid understanding of prompt engineering, evaluation methodologies, and AI safety/guardrails
• Programming: Expert-level proficiency in Python; strong skills in at least one additional language (Java, TypeScript/Node.js, C#/.NET)
• Cloud & Infrastructure: Hands-on experience deploying and operating AI workloads on cloud platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), and CI/CD tooling (Jenkins, GitHub Actions)
• Data: Proficiency with SQL and NoSQL databases, vector databases (Pinecone, Weaviate, pgvector, ChromaDB), and data pipeline tools
• API Development: Proven track record building production APIs (REST, GraphQL, gRPC) and event-driven integrations
• AI Tools Proficiency: Advanced daily usage of AI coding assistants with demonstrated impact on productivity and code quality
• Testing: Strong testing practices for AI systems including model evaluation, integration testing, and automated quality checks
• Communication: Excellent written and verbal communication skills with ability to explain complex AI concepts to diverse audiences

Preferred Qualifications

• Experience building enterprise AI platforms serving multiple product teams
• Familiarity with custom model training, fine-tuning (LoRA, QLoRA), and RLHF techniques
• Experience with agent-based AI architectures and autonomous multi-step workflows
• Knowledge of AI security concerns (prompt injection, data leakage, model poisoning) and mitigation strategies
• Experience in regulated industries (insurance, finance, healthcare) with security and compliance requirements
• Contributions to open-source AI/ML projects or published technical content
• Cloud certifications (AWS Solutions Architect, GCP Professional Cloud Architect, or equivalent)
• Experience with real-time inference, streaming responses, and low-latency AI serving architectures
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