AI Architect

AI Architect - GenAI, SLM & AI Practice

Position Overview :

We are looking for a hands-on AI Architect who can lead and build our AI/GenAI practice, with a strong focus on designing and developing domain-specific Small Language Models (SLMs) and enterprise AI solutions.

The ideal candidate will have deep practical experience across LLMs, SLMs, RAG, AI Agents, model training/fine-tuning, data engineering and cloud AI platforms. The person should be capable of taking an industry-specific business problem, determining the appropriate AI/model approach, defining the required components, building the model/solution, and taking it into production.

This role requires someone who can architect as well as engineer not just consume existing foundation models or build applications on top of APIs.

Key Responsibilities :

1. Lead AI & GenAI Practice :

- Define and drive the organization's AI/GenAI technology strategy and practice.

- Establish AI architecture standards, reusable frameworks, accelerators and reference architectures.

- Identify high-value AI opportunities across different industries and customer environments.

- Lead development of AI solutions from ideation - PoC - model development - production deployment.

- Build and mentor AI/ML/GenAI engineering teams.

- Provide technical leadership for AI solutioning, proposals and customer engagements.

- Establish best practices for AI governance, security, evaluation, responsible AI and cost optimization.

2. Domain-Specific SLM Architecture & Development :

A key responsibility of this role is to design and build Small Language Models for specific industries/domains. The candidate should be able to independently determine and define the key components required to build a domain-specific SLM, including:

- Define the business and industry problem that the SLM needs to solve.

- Determine whether an SLM, LLM, RAG or a combination of these is the appropriate approach.

- Define the required model architecture and model size based on the use case.

Evaluate whether to :

1. Build a model from scratch

2. Adapt an existing open-source foundation model

3. Fine-tune an existing LLM/SLM

4. Use knowledge distillation

5. Use LoRA/PEFT or other parameter-efficient techniques

6. Combine an SLM with RAG and external tools

- Define the required industry/domain-specific datasets.

- Establish data acquisition, cleaning, normalization, deduplication and preparation processes.

- Define data labeling and annotation requirements.

- Establish the required tokenizer, vocabulary and domain terminology strategy where applicable.

- Define training, validation and test datasets.

- Select appropriate model architecture, parameters and training methodology.

- Define training infrastructure, GPU requirements and distributed training requirements.

- Establish model evaluation benchmarks specific to the industry/use case.

- Define accuracy, hallucination, latency, throughput and cost targets.

- Design model compression, quantization and optimization strategies.

- Design inference architecture for cloud, private cloud or edge deployment.

- Establish model monitoring, evaluation and continuous improvement mechanisms.

3. SLM Design & Engineering:

The candidate should have hands-on experience with several aspects of the SLM lifecycle: Data - Model - Training - Fine-Tuning - Evaluation - Optimization - Deployment - Monitoring. Responsibilities include:

- Design domain-specific datasets for model training and fine-tuning.

- Develop data pipelines for large-scale training data preparation.

- Select and evaluate suitable open-source/base models.

- Fine-tune models for domain-specific terminology, reasoning and tasks.

- Implement parameter-efficient fine-tuning techniques such as LoRA/QLoRA/PEFT.

- Explore knowledge distillation from larger models into smaller models.

- Apply quantization and model compression to reduce inference cost.

- Optimize models for latency, memory consumption and throughput.

- Design efficient inference architectures.

- Build evaluation frameworks to compare models and approaches.

- Establish automated model testing and regression evaluation.

- Implement continuous model improvement based on production feedback.

4. LLM, RAG & Agentic AI:

In addition to SLM development, the candidate should have strong hands-on experience with:

- Large Language Models (LLMs)

- RAG architectures

- Embeddings and vector databases

- Semantic and hybrid search

- Prompt and context engineering

- AI Agents and Agentic workflows

- Tool/function calling

- Multimodal AI

- Model routing and model selection

- LLM evaluation

- Guardrails and hallucination mitigation

The architect should be capable of determining the optimal architecture, for example: SLM + RAG + Enterprise Data + AI Agents + Business APIs, rather than defaulting to a large commercial LLM for every use case.

5. Industry-Specific AI Solutions:

- Work with domain experts to understand industry-specific terminology, workflows and business processes.

- Convert industry knowledge into structured datasets, training data, knowledge bases and evaluation benchmarks.

- Design AI solutions optimized for specific industries such as Healthcare, Financial Services, Insurance, Retail, Manufacturing, Education or Technology.

- Develop reusable domain-specific AI components that can be adapted across customers.

- Identify opportunities where smaller specialized models can provide better accuracy, privacy, latency and cost efficiency than general-purpose LLMs.

Key SLM Architecture Components:

The candidate should be capable of defining and architecting the complete SLM ecosystem, including:

1. Business / Industry Use Case

2. Domain Knowledge & Taxonomy

3. Training Data Strategy

4. Data Collection & Data Pipeline

5. Data Cleaning & Preparation

6. Data Annotation / Labeling

7. Tokenizer & Vocabulary Strategy

8. Base Model Selection

9. Model Architecture / Model Size

10. Pre-training / Continued Pre-training

11. Fine-Tuning / PEFT / LoRA / QLoRA

12. Knowledge Distillation

13. RAG / Knowledge Retrieval Layer

14. Evaluation & Benchmarking Framework

15. Safety, Guardrails & Security

16. Model Compression / Quantization

17. Inference Engine

18. GPU / Compute Infrastructure

19. Model Serving & APIs

20. MLOps / LLMOps

21. Monitoring & Observability

22. Continuous Training / Model Improvement

The candidate should be able to make architectural decisions around which of these components are required for a particular use case and why, rather than applying the same architecture to every problem.

Cloud & AI Infrastructure:

Strong experience with one or more major cloud platforms:

- AWS

- Microsoft Azure

- Google Cloud

Experience with:

- GPU-based AI infrastructure

- Kubernetes / containerized AI workloads

- Serverless architectures where appropriate

- AI/ML managed services

- Model serving and inference platforms

- Distributed model training

- CI/CD and MLOps/LLMOps

- Model monitoring and observability

- Vector databases and enterprise search

Required Technical Skills:

AI / ML:

- Strong understanding of Machine Learning and Deep Learning.

- Strong understanding of Transformer architecture and modern language models.

- Hands-on experience with LLMs and SLMs.

- Experience with model training, fine-tuning and evaluation.

- Experience with PyTorch and/or TensorFlow.

- Strong Python programming skills.

SLM / LLM:

Hands-on experience with technologies/concepts such as:

- Llama / Mistral / Qwen / Gemma / Phi or equivalent open-source models

- Hugging Face ecosystem

- Transformers

- PEFT / LoRA / QLoRA

- Knowledge Distillation

- Quantization

- Model pruning/compression

- vLLM / TensorRT-LLM or equivalent inference technologies

- Model evaluation frameworks

RAG & GenAI:

- LangChain

- LlamaIndex

- LangGraph / Semantic Kernel or equivalent

- Vector databases

- Embeddings

- Hybrid search

- Reranking

- Context engineering

- Agentic AI

Required Experience:

- 8+ years of overall technology/software experience.

- Significant hands-on experience in AI/ML/GenAI engineering.

- Demonstrated experience building and deploying LLM/SLM-based solutions.

- Proven experience developing or adapting domain-specific language models.

- Experience designing training/fine-tuning datasets.

- Experience with model training, evaluation and optimization.

- Experience building production-grade AI applications.

- Strong cloud engineering experience, preferably AWS and/or Azure.

- Experience leading technical teams and defining AI architecture standards.

Strongly Preferred:

- Experience building an SLM from the ground up or through continued pre-training/adaptation of an open-source foundation model.

- Experience creating a domain-specific model for a particular industry.

- Experience with large-scale GPU infrastructure and distributed training.

- Experience with model distillation and compression.

- Experience deploying models in private/on-premise environments where data privacy is important.

- Experience building AI products/platforms rather than only PoCs.

- Experience working directly with enterprise customers and CXO-level stakeholders.

Leadership Expectations:

The ideal candidate should be able to:

- Define the AI/SLM strategy for the organization.

- Lead a team of AI/ML engineers.

- Decide build vs. buy vs. fine-tune vs. RAG.

- Define the complete architecture and technical components required for an SLM.

- Build the first working prototype personally when required.

- Guide engineers from PoC to production.

- Establish reusable AI engineering frameworks.

- Evaluate emerging AI technologies and determine their practical business value.

- Balance model quality, compute requirements, latency, privacy and cost.

Success Measures:

- Successful establishment and growth of the organization's AI practice.

- Number of AI use cases taken from concept to production.

- Successful development/adaptation of domain-specific SLMs.

- Development of reusable AI/SLM accelerators and frameworks.

- Improvement in AI solution cost, latency and performance.

- Customer adoption and revenue generated from AI solutions.

- Development of internal AI engineering capabilities.

- Successful transition of AI PoCs into scalable production platforms.

Ideal Candidate:

We are looking for an AI Architect who is also a strong AI engineer - someone who can move beyond using ChatGPT/LLM APIs and actually understand and engineer the underlying AI stack. The ideal candidate should be able to answer: Given a specific industry problem, what AI architecture should we build, what data and model do we need, should we use an LLM, SLM or RAG, how should we train/fine-tune the model, what infrastructure is required, and how do we take it into production? They should be capable of defining the architecture, building the PoC, leading the engineering team and ultimately establishing a scalable AI capability for the organization.

Skills

Agentic AI, Artificial Intelligence, Generative AI, Machine Learning, RAG, LLM, LORA, LangChain, Technical Architect, Python, Data Modeling