Senior AI Engineer

We are seeking a highly experienced and technically accomplished Senior AI Engineer with 10+ years of enterprise software engineering, machine learning, artificial intelligence, and distributed systems experience to architect, design, develop, and deploy highly scalable, production-grade AI platforms.

The ideal candidate will possess deep expertise in Large Language Models (LLMs), Generative AI, Agentic AI, deep learning, transformer architectures, Retrieval-Augmented Generation (RAG), multi-agent orchestration, AI reasoning systems, and cloud-native AI infrastructure.

This position requires extensive hands-on experience developing sophisticated AI applications, optimizing foundation models, implementing autonomous agent workflows, and integrating intelligent systems with complex enterprise platforms.

Key Responsibilities

1. Advanced AI Architecture and Foundation Model Engineering

• Architect and implement enterprise-grade artificial intelligence platforms utilizing advanced machine learning algorithms, deep neural networks, transformer architectures, and distributed model-serving infrastructure.

• Implement and optimize attention mechanisms, including Multi-Head Attention (MHA), Multi-Query Attention (MQA), Grouped-Query Attention (GQA), FlashAttention, and Rotary Positional Embeddings (RoPE).

• Develop custom neural network architectures utilizing PyTorch, TensorFlow, JAX, Hugging Face Transformers, and distributed training frameworks.

• Implement advanced model adaptation techniques including supervised fine-tuning (SFT), LoRA, QLoRA, parameter-efficient fine-tuning (PEFT), knowledge distillation, and domain-adaptive training.

• Optimize model inference performance using quantization, speculative decoding, KV-cache optimization, continuous batching, tensor parallelism, and pipeline parallelism.

• Evaluate and integrate commercial and open-weight foundation models, including GPT, Claude, Gemini, Llama, Qwen, Mistral, and Gemma, based on application requirements, latency, throughput, accuracy, and cost.

2. Generative AI and Advanced LLM Application Development

• Design and develop sophisticated Generative AI applications utilizing large language models, multimodal foundation models, and advanced prompt engineering techniques.

• Implement structured generation, constrained decoding, function calling, tool execution, and schema-based output validation for reliable enterprise AI applications.

• Develop advanced reasoning workflows utilizing planning, decomposition, reflection, self-correction, and iterative verification mechanisms.

• Engineer context-management strategies involving semantic compression, dynamic context assembly, prompt caching, and long-context optimization.

• Implement enterprise-grade LLM evaluation frameworks to measure factual accuracy, reasoning performance, groundedness, hallucination rates, toxicity, latency, and token utilization.

3. Agentic AI and Autonomous Multi-Agent Systems

• Architect and implement advanced Agentic AI platforms capable of autonomous task planning, dynamic decision-making, tool execution, and complex workflow orchestration.

• Develop multi-agent systems utilizing LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, and custom Python-based orchestration frameworks.

• Design and develop agent memory architectures incorporating short-term conversational memory, persistent long-term memory, semantic memory, episodic memory, and structured state management.

• Integrate Model Context Protocol (MCP) clients and servers to securely expose enterprise tools, APIs, databases, and business services to AI agents.

• Develop secure agentic transaction workflows utilizing Temporal, Saga orchestration, idempotency, transactional outbox patterns, and compensating transactions..

4. Retrieval-Augmented Generation (RAG) and Enterprise Knowledge Systems

• Architect advanced Retrieval-Augmented Generation platforms integrating structured, semi-structured, and unstructured enterprise knowledge sources.

• Implement hybrid retrieval architectures combining dense vector embeddings, sparse retrieval, BM25, semantic search, and metadata-based filtering.

• Develop multi-stage retrieval pipelines incorporating query rewriting, query decomposition, reranking, contextual retrieval, and relevance optimization.

• Design Graph RAG architectures integrating knowledge graphs, graph databases, entity resolution, relationship extraction, and graph traversal.

• Implement vector search solutions using Pinecone, Milvus, Weaviate, FAISS, Elasticsearch, OpenSearch, and PostgreSQL with pgvector.

• Develop enterprise document intelligence pipelines incorporating document parsing, OCR, semantic chunking, hierarchical indexing, and contextual metadata enrichment.

• Implement knowledge ingestion and synchronization pipelines supporting incremental updates, change data capture, data lineage, access controls, and document versioning.

• Evaluate retrieval quality using Recall@K, Precision@K, Mean Reciprocal Rank (MRR), NDCG, answer relevance, faithfulness, and citation correctness.

5. Machine Learning, Deep Learning, and Predictive AI

• Design and implement advanced supervised, unsupervised, self-supervised, and reinforcement learning solutions for complex enterprise use cases.

• Develop predictive modeling pipelines utilizing XGBoost, LightGBM, Random Forest, Support Vector Machines, gradient boosting, and deep neural networks.

• Implement distributed model training and experimentation using PyTorch Distributed, DeepSpeed, Ray Train, and distributed data-processing frameworks.

• Apply explainable AI methodologies including SHAP, LIME, feature attribution, model interpretability, and model fairness analysis.

• Establish rigorous model-validation practices including cross-validation, drift analysis, calibration, robustness testing, and out-of-distribution evaluation.

6. Cloud AI Platforms and Infrastructure Engineering

• Design cloud-native AI solutions utilizing Amazon Bedrock, Amazon SageMaker, Azure AI Foundry, Azure Machine Learning, Google Vertex AI, and managed foundation model services.

• Implement GPU-accelerated computing environments utilizing NVIDIA CUDA, cuDNN, NCCL, and distributed GPU clusters..ip

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a closely related technical discipline.

• 10+ years of professional experience in software engineering, distributed systems, data engineering, machine learning, or artificial intelligence, including substantial recent hands-on experience delivering production AI solutions.

• Advanced proficiency in Python, object-oriented programming, asynchronous programming, data structures, algorithms, and distributed application development.

• Strong experience with PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, and modern machine learning frameworks.

• Demonstrated expertise in developing Generative AI applications, LLM-powered solutions, RAG architectures, and autonomous agent workflows.

• Extensive experience with cloud computing, container orchestration, microservices architecture, distributed messaging, and production AI infrastructure.

• Strong understanding of transformer architectures, neural network optimization, vector databases, embedding models, and advanced information retrieval.

• Proven ability to design and implement secure, scalable, fault-tolerant enterprise AI systems.

• Excellent analytical, architectural, troubleshooting, technical leadership, and communication skills.