AI Engineer

Job Summary

Mindstix is seeking AI Engineers to build Generative AI applications such as LLM-powered AI Agents, Copilots, and Automation Workflows. This is a hands-on engineering role focused on leveraging frontier AI models to create secure, reliable, and scalable Agentic AI solutions for the modern enterprise. You will work on our cutting-edge AI stack, cloud-native AI platforms, and large-scale datasets to deliver innovation for leading global companies.

Responsibilities

• Integrate with modern LLMs and foundation models to design and build next-gen applications: AI agents, enterprise copilots, conversational AI bots, personalization and recommendation engines, and insights engines.
• Build and optimize RAG pipelines using modern AI orchestration frameworks - Data ingestion, chunking, embeddings, vector storage, prompt engineering, retrieval strategies, and response synthesis.
• Finetune or adapt foundation models with instruction tuning, hyperparameter tuning, sampling, and retrieval strategies for specific customer domains and use cases.
• Integrate AI capabilities into the modern enterprise ecosystem: Implement MCPs and Agent-to-Agent communication protocols.
• Implement observability and evaluation for AI systems: Tracing, quality metrics, evaluation datasets, feedback capture, and guardrails.
• Inference optimization: Optimize latency, throughput, and costs for LLM-backed solutions; Experiment with model sizes, caching, batching, and routing strategies.
• Collaborate on AI solution design with Product Managers and Designers to shape innovative ideas, refine product requirements, and to design human-AI interactions.
• Collaborate with Data Engineers / MLOps teams to operationalize AI solutions, automate training workflows, and manage production environments.
• Stay current with cutting-edge developments in Generative AI, frontier models, orchestration frameworks, vector databases, and tools for LLMOps, bringing best practices to the AI SDLC lifecycle.

Who Fits Best

• You are a passionate engineer who enjoys solving complex, open-ended problems with real-world constraints.
• You enjoy building AI solutions from demos to production - owning safety, reliability, performance, and maintainability.
• You thrive in a fast-paced dynamic environment, comfortable with context-switching across multiple ideas and projects.
• You are curious about the end-to-end stack, from data pipelines and vector stores to API design and user interactions.
• You care about engineering discipline: clean abstractions, test automation, observability, and security.
• You embrace a mindset of constant learning and evolution as the AI landscape rapidly evolves and to share your learnings with our thriving community.

Our AI Engineering Stack

• Core Software Engineering: Strong in Python or TypeScript/Node.js; Solid understanding of data structures, algorithms, and foundations of distributed systems.
• REST/gRPC APIs, microservices, message queues, and cloud-native architecture.
• Generative AI, LLMs, Foundation Models: OpenAI, Azure OpenAI Service, AWS Bedrock, Anthropic, Google Vertex AI or similar hyperscaler platforms. Open-source models (Llama, Mistral) and serving stacks.
• Prompt engineering, hyperparameter tuning, tool/function calling, and basic fine-tuning.
• AI Orchestration Frameworks Libraries: PyTorch, TensorFlow, Hugging Face Transformers; LangChain, LlamaIndex, or similar orchestration frameworks.
• Data Platforms: Vector databases - Pinecone, Weaviate, Qdrant, pgvector/FAISS for semantic search and RAG.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.