Machine Learning Lead – Agentic AI Systems

About Us

We are building an AI-native platform focused on intelligent automation and privacy-aware AI workflows. Our systems combine LLM reasoning, agent orchestration, multimodal understanding, and structured ML techniques to enable reliable enterprise AI execution at scale.

About the Role

We are looking for a senior ML Lead with strong hands-on experience in Agentic AI systems, LLM orchestration, and autonomous workflow design.

This is a deeply technical, hands-on leadership role. You will design and build autonomous AI engines capable of executing complex tasks with minimal to zero manual intervention, while ensuring reliability, determinism, and performance at scale.

We value strong engineering fundamentals (6+ years) along with 2+ years of production experience working with LLMs and agent-based systems.

WhatYoullBuild

Advanced prompt engineering systems enabling LLMs to:

oPerform complex multi-step reasoning

oStrictly adhere to defined output schemas

oExecute structured decision logic

Fully autonomous agent systems capable of completing tasks with zero manual intervention

Dynamic system-prompt construction engines that adapt prompts in real-time based on user input and context

Multi-agent orchestration frameworks with:

oTool calling

oMemory management

oReasoning chains

oPlannerexecutor architectures

Mechanisms to control LLM unpredictability and inconsistent behavior through:

oGuardrails

oOutput validation layers

oDeterministic prompting strategies

oSelf-reflection/ critiqueloops

oEvaluation pipelines

Intelligent ingestion and structured understanding of complex, heterogeneous data sources across multiple formats and modalities

Robust multilingual AI pipelines capable of reasoning and executing tasks across diverse languages and regional contexts

Advanced data transformation and controlled data generation frameworks for testing, validation, and system robustness

High-performance inference pipelines using:

oOpen-source LLMs and reasoning models

oGPU-optimized workloads

oBatch inference and parallel execution strategies

oScalable architectures for high-throughput AI execution

Key Responsibilities

Architect and lead development of production-grade Agentic AI systems

Design advanced prompt engineering frameworks for complex task execution

Build autonomous AI engines with dynamic system prompt generation

Develop strategies to mitigate hallucination and model inconsistency

Design multimodal ingestion pipelines for structured extraction and reasoning

Enable multilingual AI reasoning capabilities across workflows

Design synthetic and transformation-based data pipelines to improve evaluation coverage and system generalization

Integrate open-source LLMs and reasoning models into scalable pipelines

Optimizeinference performance using GPU acceleration and distributed systems

Design evaluation frameworks to measure reasoning quality, determinism, and reliability

Own the ML layer architecture and long-term AI roadmap

Mentor engineers on LLM best practices, agent design patterns, and AI reliability

WhatWereLooking ForRequired

6+ years overall software/ML engineering experience

2+ years hands-on experience building LLM-powered systems in production

Strong experience building autonomous agent-based systems

Deepexpertisein prompt engineering for structured, deterministic outputs

Experience designing dynamic prompt construction pipelines

Experience handling LLM unpredictability and improving response consistency

Strong knowledge of:

oEmbeddings and vector databases

oRAG architectures

oPlannerexecutor agent models

oMemory-augmented agents

Experience designing structured extraction pipelines across complex document types and semi-structured data

Experience working with multilingual models and cross-language reasoning systems

Experience building controlled data generation or transformation systems for robustness testing

Experience working with open-source LLMs and reasoning models

Strong Python and backend system design skills

ExperienceoptimizingML workloads for GPU-based high-throughput inference

Knowledge of distributed systems and scalable inference architectures

Nice to Have

Experience building privacy- or security-aware AI systems

Exposure to compliance frameworks (GDPR, HIPAA)

Experience fine-tuning LLMs or training domain-specific models

Experience designing model evaluation benchmarks and automated regression testing

Experience working with multimodal or vision-language models

WhatYoullGain

Ownership of next-generation autonomous AI systems

Opportunity to design and scale enterprise-grade Agentic AI from the ground up

Direct collaboration with founders and senior engineering leaders

Budget and freedom to experiment with emerging AI models and architectures

Ability to build real-world, production AI systems that solve complex enterprise problems

Compensation & Benefits

Competitive senior-level compensation

Flexible work culture

Research & experimentation budget for AI innovation