AI / ML lead Engineer

JOB DESCRIPTION

About the Company Kalo Tech Studio is a technology and product development company specialising in AI-powered applications, enterprise software solutions, and digital platforms. We build scalable, intelligent software products for global markets.

QualificationMasters / B.Tech / B.E in Computer Science, AI, or related engineering discipline

Age preference28 - 38 years (preferred)

Work Experience8+ years in software engineering with 4+ years focused on AI/ML systems

Role Summary Seeking a highly experienced Head of AI Engineering to lead the strategy, architecture, and delivery of enterprise AI initiatives across Kalo Tech Studio. The role combines deep technical leadership with client engagement and business strategy execution. The ideal candidate will own end-to-end AI architecture and delivery including LLM systems, multi-agent frameworks, RAG pipelines, multimodal AI applications, MLOps infrastructure, and enterprise AI governance. This individual will work directly with senior leadership and clients to translate complex business requirements into scalable AI solutions while building and mentoring a world-class AI engineering team.

Role and ResponsibilitiesClient & Business Engagement

1. Lead client-facing discovery sessions alongside senior leadership to understand business objectives and AI transformation opportunities.
2. Translate business requirements into scalable AI/ML solution architectures and delivery roadmaps.
3. Present AI strategies, technical recommendations, and implementation plans to client stakeholders and executive leadership.
4. Build trusted relationships with client technical and business teams throughout project execution.

AI Architecture & Technical Leadership

1. Own end-to-end AI/ML architecture across projects including data pipelines, model selection, deployment infrastructure, monitoring, and governance.
2. Design and implement Generative AI solutions including LLM architectures, RAG systems, multi-agent frameworks, and multimodal AI workflows.
3. Architect multi-model routing systems, prompt orchestration frameworks, and AI cost optimisation strategies.
4. Build AI workflow automation pipelines and integrate multilingual/Indic AI models including Hindi, Marathi, Telugu, Kannada, and Tamil language support.
5. Build Retrieval Augmented Generation (RAG) pipelines and enterprise knowledge systems using vector databases and embedding architectures.
6. Design AI-assisted automation and configuration engines using natural language interfaces.
7. Define and enforce AI safety, governance, explain ability, confidence scoring, hallucination mitigation, and human-in-the-loop standards.
8. Evaluate and integrate emerging AI/ML tools, frameworks, and research aligned with product and client requirements.
9. Make strategic architecture decisions across frameworks, cloud platforms, hosting models, and AI deployment strategies.

Delivery & Engineering Excellence

1. Own AI/ML project delivery from architecture through production deployment while ensuring quality, scalability, and reliability.
2. Collaborate with full-stack engineers, product managers, data engineers, and business analysts to embed AI capabilities into live enterprise systems.
3. Ensure models are production-ready with proper versioning, monitoring, observability, CI/CD pipelines, and drift detection mechanisms.
4. Identify technical and delivery risks proactively and drive resolution plans effectively.

Team Building & Capability Development

1. Hire, mentor, and grow high-performance AI/ML engineering teams.
2. Conduct architecture reviews, technical evaluations, and knowledge-sharing sessions across engineering functions.
3. Establish AI engineering standards, MLOps practices, evaluation frameworks, and internal capability development programmes.
4. Drive a culture of technical rigour, innovation, ownership, and delivery accountability across the AI organisation.

Evaluate and integrate new AI/ML tools, models, and research relevant to product goals

Key Technical/ Functional Competencies

1. Strong expertise in LLM APIs, agentic AI systems, Lang Chain, Lang Graph, Crew AI, Hugging Face, and transformer-based architectures (GPT, LLaMA, BERT, T5).
2. Deep experience in Python ecosystem including PyTorch, FastAPI, Celery, model training, fine-tuning, evaluation, and production deployment.
3. Hands-on experience with RAG systems, vector databases (Pinecone, FAI…