Applied AI / Machine Learning Engineer

Location: Bhopal, Madhya Pradesh

About Lear Labs

Lear Labs is an enterprise AI lab that builds production AI systems for organizations across India and beyond. We are headquartered in Bangalore, with projects and partners spanning across the world.

We work on problems where AI has to operate in the real world, not just inside a demo. Our work spans computer vision, machine learning, agentic AI, large language models, data systems, automation, and enterprise software.

Our partners include large enterprises, infrastructure organizations, conglomerates, and government bodies. We take projects from raw data and early experimentation all the way to systems that people use operationally.

About the Role

We are looking for an Applied AI / Machine Learning Engineer based in Bhopal to work closely with our engineering and operations teams on one of our major infrastructure AI projects.

A key part of the work involves a road and infrastructure intelligence platform that processes large volumes of imagery and video collected from vehicles in the field. The system identifies road conditions and infrastructure assets, converts them into structured and geo-referenced data, and produces dashboards and reports used by operational teams and decision-makers.

However, this is not exclusively a computer vision role .

Depending on your strengths, you may also work on:

LLM and agentic AI systems

AI-powered data processing and automation

multimodal AI involving images, video, documents, and structured data

retrieval and knowledge systems

backend services supporting AI applications

evaluation and monitoring of production AI systems

internal AI tools for field and operations teams

We are looking for someone who enjoys building things, experimenting quickly, debugging real systems, and understanding how AI behaves outside controlled datasets.

You do not need to be a senior ML researcher or have experience with every technology listed below.

Strong fundamentals, curiosity, engineering ability, and evidence that you have actually built things matter more.

What You Might Work On

Computer Vision

Train, fine-tune, evaluate, and improve models for detecting road defects, infrastructure assets, objects, and other conditions from real-world imagery and video.

This may involve YOLO or similar detection models, segmentation, image classification, tracking, and multimodal vision models.

Video and Image Processing

Work with continuous footage collected from moving vehicles.

You may deal with:

frame extraction and sampling

image quality

motion blur

changing lighting

camera positioning

resolution and compression

object tracking across frames

duplicate detection

geo-referencing observations

The data will not always be clean. Part of the job is figuring out what can realistically be extracted from it.

Training Data and Evaluation

Help us improve datasets rather than treating model training as a black box.

This includes:

reviewing labels

defining annotation standards

identifying bad or ambiguous training examples

handling class imbalance

finding failure cases

creating train, validation, and test datasets

comparing models and thresholds

measuring precision and recall

checking whether a new model is actually better before deploying it

Agentic AI and LLM Systems

You may also work on AI agents and LLM-powered workflows around the platform.

Examples could include:

agents that analyse infrastructure findings

automated report generation

natural-language querying of survey data

document and knowledge retrieval

AI workflows that combine databases, APIs, images, maps, and documents

structured extraction from unstructured information

tool-calling agents

evaluation of LLM and agent behaviour

Experience with frameworks such as LangGraph, LangChain, CrewAI, OpenAI Agents SDK, Google ADK, or similar is useful, but understanding the underlying concepts matters more than knowing a particular framework.

Data Pipelines

Help process large amounts of imagery, video, metadata, and model outputs.

You may work on:

Python data pipelines

APIs

databases

cloud storage

batch processing

dataset versioning

GPU workloads

model inference pipelines

You should be comfortable investigating why a pipeline is slow, why data is missing, or why an output does not make sense.

Production AI

Models are useful only when the rest of the system works.

You may help with:

deploying models

building inference APIs

Docker

cloud GPUs

logging and monitoring

debugging production failures

improving inference speed and cost

connecting AI systems with existing applications

Core Requirements

Strong Python fundamentals.

Practical experience with machine learning, computer vision, LLMs, agentic AI, or a combination of these .

Ability to understand and modify existing code rather than only use no-code tools.

Familiarity with PyTorch, TensorFlow, or another ML framework.

Comfortable working with APIs, structured data, and basic backend systems.

Ability to debug problems independently.

Comfortable using Git.

Willingness to work hands-on with imperfect real-world datasets.

Ability to explain technical findings clearly to engineers and non-technical team members.

Comfortable communicating in English and spoken Hindi.

Willingness to occasionally visit field locations and understand how data is actually being collected.

Particularly Relevant Experience

Any of the following would strengthen your application:

YOLO or other object detection models.

Image segmentation.

Multi-object tracking.

OpenCV.

Vision Transformers or multimodal models.

PyTorch.

Fine-tuning models on custom datasets.

LLM APIs such as OpenAI, Anthropic, Gemini, or open-source models.

RAG systems.

AI agents and tool calling.

LangGraph, LangChain, CrewAI, Google ADK, OpenAI Agents SDK, or similar frameworks.

FastAPI, Flask, or Python backend development.

MongoDB, PostgreSQL, vector databases, or similar systems.

Docker and Linux.

AWS, Azure, or GCP.

GPU-based model training or inference.

Hugging Face.

ONNX or TensorRT.

MLflow, Weights & Biases, DVC, or similar tools.

You do not need experience with all of these.

Someone who is strong in computer vision but has never built an AI agent can still be a good fit.

Someone who has built strong LLM and agentic systems and has some ML or vision exposure can also be a good fit.

The Kind of Person We Are Looking For

We value builders.

You may be a good fit if you are the kind of person who has:

trained a model because you were curious whether it would work

built an AI agent that actually calls tools rather than just chatting

collected or labelled your own dataset

deployed a model or API yourself

built something for a college competition, hackathon, robotics team, or startup

used open-source models and experimented beyond tutorials

spent a night debugging something because you wanted to understand why it was failing

participated in SAE BAJA, Formula Student, robotics, drone, autonomous vehicle, computer vision, or similar engineering projects

A strong GitHub profile, personal project, competition project, internship, freelance build, or serious college project can matter as much as formal work experience.

Experience Level

We are open to candidates at different stages of their careers.

You could be:

an engineer with 1 to 4 years of experience

a strong recent graduate

someone coming from a startup or applied AI role

someone with a strong engineering or computer science background who has built unusually good projects

We care more about what you can build and how you think than the number of years written on your CV.

What You Will Get

Real production work. Your models and systems will work on actual field data and be used by operational teams.

Broad AI exposure. Work across computer vision, machine learning, multimodal AI, LLMs, agents, and production systems.

Direct access to the founders and senior engineers. You will be close to product and technical decisions rather than several layers away from them.

Fast learning. You will work on problems involving software, AI, hardware, field operations, data, and real customers.

Serious compute. Access to GPU infrastructure and the hardware required for ML experimentation.

Ownership. Good engineers here are given problems to solve, not tickets to execute.

Room to grow. As the team expands, strong performers can take ownership of increasingly larger parts of the AI platform.

Competitive compensation. Compensation is discussed individually based on experience, ability, and the value you can bring.

Location

This role is based primarily in Bhopal .

Our road-survey and infrastructure operations are run from Bhopal, which means you will be physically close to the people collecting data and using the system.

For this project, that matters.

You may occasionally go into the field to see how cameras are mounted, how vehicles operate, how footage is collected, and what conditions look like in the real world.

We believe engineers building AI from physical-world data should understand how that data is actually produced.

Seniority level: Entry level

Employment type: Full-time

Job function: Engineering and Information Technology

Industries: Technology, Information and Internet