Pune

About Us

We empower enterprises globally through intelligent, creative, and insightful services for data integration, data analytics and data visualization.

Hoonartek is a leader in enterprise transformation, data engineering and an acknowledged world-class Ab Initio delivery partner.

Using centuries of cumulative experience, research and leadership, we help our clients eliminate the complexities & risk of legacy modernization and safely deliver big data hubs, operational data integration, business intelligence, risk & compliance solutions and traditional data warehouses & marts.

At Hoonartek, we work to ensure that our customers, partners and employees all benefit from our unstinting commitment to delivery, quality and value. Hoonartek is increasingly the choice for customers seeking a trusted partner of vision, value and integrity

How We Work?

Define, Design and Deliver (D3) is our in-house delivery philosophy. It’s culled from agile and rapid methodologies and focused on ‘just enough design’. We embrace this philosophy in everything we do, leading to numerous client success stories and indeed to our own success.

We embrace change, empowering and trusting our people and building long and valuable relationships with our employees, our customers and our partners. We work flexibly, even adopting traditional/waterfall methods where circumstances demand it. At Hoonartek, the focus is always on delivery and value.

Job Description

AI Platform Architecture

- Define and own the end-to-end architecture of the organisation's AI platform — spanning model development, training infrastructure, serving, monitoring, and governance
- Design scalable, cloud-native AI infrastructure on GCP (Vertex AI), Azure (Azure AI / ML), or AWS (SageMaker) supporting both training and real-time inference workloads
- Establish reference architectures for generative AI applications including RAG pipelines, agentic systems, and multi-modal AI workflows
- Architect model serving infrastructure — including GPU cluster management, batching strategies, model caching, and latency-optimised inference endpoints
- Evaluate and select AI frameworks, tooling, and managed services — balancing build vs. buy, cost, scalability, and strategic fit

Generative AI & LLM / SLM Systems

- Lead the architecture of generative AI systems leveraging LLMs (GPT-4, Gemini, Claude, Llama, Mistral) and SLMs (Phi-3, Gemma, TinyLlama) for production use cases
- Design and implement enterprise-grade RAG architectures — including chunking strategies, embedding models, vector database selection (Pinecone, Weaviate, pgvector, Vertex AI Vector Search), re-ranking, and hybrid retrieval
- Architect agentic AI systems with tool use, memory, planning, and multi-agent orchestration using frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI
- Define prompt engineering standards, prompt management, and version-controlled prompt libraries for production systems
- Establish guardrails, content safety, hallucination mitigation, and evaluation frameworks (RAGAS, TruLens, DeepEval) for GenAI applications

Model Training & Fine-Tuning

- Architect and oversee pre-training, continued pre-training, and supervised fine-tuning (SFT) workflows for both LLMs and domain-specific models
- Design and implement parameter-efficient fine-tuning (PEFT) pipelines using LoRA, QLoRA, DoRA, and adapter-based methods using Hugging Face PEFT and TRL libraries
- Lead RLHF (Reinforcement Learning from Human Feedback) and DPO (Direct Preference Optimisation) workflows for alignment and preference tuning
- Architect distributed training infrastructure — multi-GPU and multi-node training using PyTorch FSDP, DeepSpeed ZeRO, and Megatron-LM
- Design data preparation pipelines for model training: dataset curation, deduplication,



tokenisation, instruction formatting, and quality filtering
- Establish experiment tracking, hyperparameter optimisation, and model evaluation frameworks (MLflow, Weights & Biases, Optuna)

MLOps & Model Lifecycle

- Define and implement the full MLOps lifecycle — from experiment management through model registry, staging, shadow deployment, A/B testing, and production monitoring
- Design model observability frameworks: drift detection, performance degradation alerting, data quality monitoring, and automated retraining triggers
- Architect CI/CD pipelines for ML — automated training, evaluation gating, model promotion, and rollback procedures
- Lead the adoption of feature stores (Feast, Tecton, Vertex AI Feature Store) for consistent feature management across training and serving

AI Governance & Responsible AI

- Establish the organisation's responsible AI framework — covering fairness, explainability, bias detection, privacy-preserving ML, and regulatory compliance
- Define model documentation standards (model cards), dataset governance, and audit trails for model lineage and versioning
- Lead AI risk assessment processes and engage with legal, compliance, and ethics stakeholders on AI system deployment decisions

Thought Leadership & Mentorship

- Act as the organisation's internal subject matter expert on AI/ML — publishing internal research notes, architecture decision records, and technical standards
- Mentor senior ML engineers and applied scientists; conduct architectural design reviews and raise the bar on AI engineering quality
- Track and evaluate emerging AI research (arXiv, NeurIPS, ICML, ICLR) and advise on adoption of applicable breakthroughs
- Represent the organisation in external forums, vendor engagements, and technical partnerships

3. Required Skills & Experience

Foundations — Machine Learning & Deep Learning

- 10+ years in AI/ML engineering or architecture roles with a demonstrable progression into platform-level or architectural responsibility
- Expert-level understanding of supervised, unsupervised, and reinforcement learning — including classical ML (gradient boosting, SVMs, ensembles) and deep learning (CNNs, RNNs, Transformers)
- Deep knowledge of the Transformer architecture — attention mechanisms, positional encoding, tokenisation, KV caching, and scaling laws
- Proficiency in PyTorch (primary) and familiarity with JAX/Flax for research-oriented workloads
- Strong mathematical foundation: linear algebra, probability, statistics, information theory, and optimisation

Generative AI — LLMs & SLMs

- Hands-on production experience with large language models — including prompt engineering, context window management, structured output, function calling, and multi-modal inputs
- Expert knowledge of the LLM landscape: GPT-4o, Gemini 1.5 Pro, Claude 3.x, Llama 3, Mistral, Mixtral, Command R+, and open-weight SLMs (Phi-3, Gemma 2, TinyLlama)
- Deep understanding of LLM inference optimisation: quantisation (GPTQ, AWQ, GGUF), speculative decoding, continuous batching, and PagedAttention (vLLM)
- Experience with model benchmarking and evaluation: MMLU, HellaSwag, HumanEval, MT-Bench, and custom domain evals

RAG — Retrieval-Augmented Generation

- Expert in designing and deploying production RAG systems — naive RAG, advanced RAG (HyDE, reranking, query decomposition), and modular RAG architectures




- Hands-on experience with embedding models: OpenAI Ada, text-embedding-3, BGE, E5, Cohere Embed — and selection criteria for domain-specific retrieval
- Proficiency with vector databases: Pinecone, Weaviate, Qdrant, Chroma, pgvector, and Vertex AI Vector Search
- Deep knowledge of chunking strategies, document parsing, metadata filtering, hybrid search (BM25 + dense), and retrieval evaluation (RAGAS, TruLens)

Fine-Tuning & PEFT — LoRA, QLoRA & Beyond

- Expert-level, hands-on experience with parameter-productive fine-tuning: LoRA, QLoRA, DoRA, IA³, prefix tuning, and prompt tuning using Hugging Face PEFT
- Experience with full fine-tuning and continued pre-training workflows for domain adaptation and instruction following (Alpaca, ShareGPT, custom instruction datasets)
- Proficiency with RLHF pipelines: reward model training, PPO-based alignment using TRL, and DPO / IPO preference optimisation
- Hands-on experience with distributed training frameworks: DeepSpeed ZeRO (Stages 1–3), PyTorch FSDP, and Megatron-LM for multi-GPU/multi-node training
- Familiarity with training infrastructure: NVIDIA A100 / H100 clusters, NVLink, NCCL, and GPU memory optimisation techniques (gradient checkpointing, mixed precision, flash attention)

MLOps & Infrastructure

- Production-grade MLOps experience: ML pipelines, model registries, A/B testing, shadow deployment, drift monitoring, and automated retraining
- Proficiency with MLOps platforms: MLflow, Weights & Biases, Kubeflow, Vertex AI Pipelines, or Azure ML
- Containerisation and orchestration: Docker, Kubernetes, and Helm for scalable model serving
- Experience with model serving frameworks: vLLM, TGI (Text Generation Inference), Triton Inference Server, BentoML, or Ray Serve
- Infrastructure-as-code proficiency: Terraform or Pulumi for reproducible AI infrastructure provisioning

Certifications & Credentials (Preferred)

- Google Cloud Professional ML Engineer or Vertex AI Specialist
- AWS Certified Machine Learning Specialty or Azure AI Engineer Associate
- Hugging Face certifications or demonstrated open-source contributions to ML repositories
- Published research (arXiv, conference proceedings) or recognised technical blog authorship in AI/ML

4. Technical Ecosystem

Foundation Models & GenAI Frameworks

- GPT-4o / GPT-4 Turbo
- Gemini 1.5 Pro / Flash
- Claude 3.x (Opus/Sonnet)
- Llama 3 / Mistral / Mixtral
- Phi-3 / Gemma 2 / TinyLlama
- LangChain / LlamaIndex
- AutoGen / CrewAI
- Semantic Kernel

Training, Fine-Tuning & PEFT

- PyTorch / FSDP
- DeepSpeed ZeRO
- Hugging Face PEFT
- TRL (SFT / DPO / PPO)
- LoRA / QLoRA / DoRA
- Flash Attention 2
- Megatron-LM
- Weights & Biases

RAG, Vector Databases & Serving

- Pinecone / Weaviate
- pgvector / Qdrant
- RAGAS / TruLens
- BGE / E5 / Ada Embeddings
- vLLM / TGI
- Triton Inference Server
- Ray Serve / BentoML
- MLflow / Kubeflow

5. Leadership & Behavioural Competencies

Systems Thinking

Designs AI systems that are robust end-to-end — from data pipeline to model to application — with production failure modes in mind.

Research Fluency

Reads and critically evaluates AI research papers; translates applicable findings into engineering practice without chasing hype.

Engineering Rigour

Brings software engineering discipline to AI — versioning, testing, observability, and reproducibility as first-class concerns.

Executive Communication

Distils complex AI concepts into crisp business narratives for C-suite and board-level stakeholders.

Responsible AI Mindset

Proactively identifies bias, fairness, safety, and privacy risks in AI systems and champions mitigation as a design principle.

Mentorship & Influence

Elevates the capability of the wider AI engineering team through design reviews, pairing, technical writing, and knowledge sharing.

📌 Sr AI Architect (India)
🏢 Hoonartek
📍 India