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Data - AI / ML Engineer
Data - AI / ML Engineer

Data - AI / ML Engineer at FreeFlow Ventures · Kolkata · 4 - 7 years · ₹15L - ₹25L / yr · Posted 11 Apr 2026

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Data - AI / ML Engineer

Agency job
4 - 7 yrs
₹15L - ₹25L / yr
Kolkata
Skills
skill iconData Science
skill iconMachine Learning (ML)
Artificial Intelligence (AI)

Data - AI / ML Engineer 

Full-Time   |   On-site, Kolkata   |   Immediate Joining   |   4+ years experience

ABOUT US 

Company Name: Freeflow ventures


We are a venture building and investment firm focused on emerging markets across India, the Middle East, and Africa. We work with early-stage startups -diagnosing gaps, structuring interventions, and preparing them for investor-readiness through a proprietary data and intelligence platform.

Our platform combines automated data verification, startup scoring, and structured workflow automation to bring consistency and credibility to early-stage investment decisions. We are at an active build and expansion phase, and this role sits at the core of that infrastructure.


ROLE OVERVIEW

We are looking for a Data - AI / ML Engineer who can own both the data pipelines that bring verified information into our platform and the intelligence models that turn that information into reliable startup scores.


This is a dual-responsibility role. You will be expected to build and maintain robust data infrastructure as well as develop, calibrate, and improve machine learning models. Both are equally important to the platform.


You will work closely with the Platform Owner and alongside a Backend Engineer who owns system integrations and workflow logic. Your work produces the scored intelligence output. The Backend Engineer's work connects that output to platform actions. The two roles are tightly interdependent and require close daily collaboration, especially in the first 30 days.


Note: You are the first technical hire on the platform team. The Backend Engineer joins the same week. Clear communication, well-defined handoff points, and shared documentation between the two of you are non-negotiable from Day 1.

WHAT YOU WILL DO

Data Pipeline

  • Build and maintain pipelines that collect, clean, and normalize data from multiple external sources into a consistent, usable format
  • Design connector architecture that allows individual data sources to be added, swapped, or removed without rebuilding the entire pipeline
  • Implement automated data quality checks that catch bad data before it reaches the scoring layer -anomaly detection, constraint enforcement, and schema validation
  • Build an automated eligibility screening system that verifies whether a startup has sufficient verified data before assessment begins
  • Ensure the pipeline is resilient -critical data signals must have backup sources so a single vendor failure does not disrupt platform output
  • Structure data storage to support different regulatory requirements across multiple countries -data from different regions must be handled according to the rules of that region


AI and Machine Learning

  • Audit the existing scoring engine before making any changes -understand what it does, how it was built, and what would be lost if it were modified
  • Calibrate scoring models against real portfolio data so that scores are meaningful, consistent, and comparable across different startup types and stages
  • Build confidence scoring logic that determines when the system is certain enough to act autonomously and when it should route to human review
  • Ensure every model output is explainable -investors must be able to see exactly which data points drove a score, not just the final number
  • Build a feedback loop so that real-world outcomes feed back into the model over time, making it progressively more accurate
  • Maintain a structured data store of assessment outputs and outcomes that the model uses to improve


Working With the Backend Engineer

  • Define a clear data contract at the handoff point -what data you produce, in what format, and what the Backend Engineer can expect to receive
  • Collaborate on trigger logic -what score thresholds or confidence drops should fire what system actions
  • Align on data schema requirements so that the APIs the Backend Engineer builds conform to the structure your pipeline produces
  • Communicate blockers early -the pipeline and backend system are built simultaneously, so delays on one side directly affect the other
  • Document everything you build so the Backend Engineer and Platform Owner can understand, debug, and extend it without depending on you for every question


WHAT WE ARE LOOKING FOR

Skills are divided into two categories. Must Have means the role cannot function without it. Good to Have means it gives you an edge.


Skill

Priority

Data Engineering

Building and maintaining data pipelines from multiple sources

Must Have

Data normalization and schema design

Must Have

Automated data quality validation

Must Have

API integration across different source types

Must Have

Pipeline orchestration and scheduling

Must Have

Cloud infrastructure -storage, compute, and deployment

Must Have

Version control and code documentation

Must Have

AI and Machine Learning

Building and calibrating supervised machine learning models

Must Have

Model explainability -making model outputs traceable and interpretable

Must Have

Confidence scoring and threshold calibration

Must Have

Experiment tracking and model versioning

Must Have

Building feedback loops that improve models over time using real-world outcomes

Must Have

Natural language processing or document understanding

Good to Have

Vector databases and semantic search

Good to Have

Collaboration and Context

Ability to define clear data contracts and handoff points with backend engineers

Must Have

Clear written documentation of pipeline logic, model decisions, and failure modes

Must Have

Prior experience working in or with early-stage startups

Good to Have

Exposure to financial data, investment platforms, or data verification systems

Good to Have


WHAT WE OFFER

  • Competitive compensation based on experience -discussed during the interview process
  • Ownership of both the data and intelligence layers from Day 1 -this is not a support or maintenance role
  • Direct access to the Platform Owner and Founder 
  • Close collaboration with a Backend Engineer from Day 1 -the two roles are designed to work as a unit
  • Work on a genuinely novel problem in an emerging market context
  • On-site Kolkata with a small, high-accountability team
  • Opportunity to scale the platform across multiple international markets





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About FreeFlow Ventures

Founded :
2019
Type
Size
Stage

About

FreeFlow is a startup accelerator that co-creates ventures, helps startups get off the ground, develops sales funnels, signs MoUs with corporate and government partners, and engages with government departments. It has hosted flagship events, established B2B networks, and committed over $16M in funding. FreeFlow is a startup accelerator that provides a range of services to help startups succeed. It co-creates ventures, helps startups get off the ground, develops sales funnels, signs MoUs with corporate and government partners, and engages with government departments. FreeFlow has a proven track record of success, having helped over 275 startups get off the ground and committed over $16M in funding. It has also established B2B networks and hosted flagship events that have attracted investors and startups from around the world. FreeFlow operates in a variety of industries, including technology, healthcare, and finance, and has a broad range of use-cases, from helping startups develop their business plans to providing funding and support for established companies looking to expand.
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• Work closely with the founder and Product Engineer to turn intelligence into product experiences.

• Design APIs and production systems that bring ML/AI capabilities into the application.

• Build evaluation, monitoring and feedback loops so the intelligence improves over time.


WHO SHOULD APPLY

• Experience: 0–4 years’ experience, including exceptional fresh graduates. Strong 1–3 year engineers and experienced 3–4 year product builders are welcome.

• Strong foundations in ML, Python, statistics and software engineering.

• Evidence of Building: Experience with AI/ML projects, recommendation systems, LLM applications or personalization is highly valued.

• Strong evidence of building: Shipped projects, research, hackathons, internships, open source or startup work. 


WHAT WE LOOK FOR

MACHINE LEARNING DEPTH

Can you understand the modelling problem underneath the application?


RECOMMENDATION & PERSONALIZATION

Can you reason about relevance, ranking, cold start and behavioural signals?


AI ENGINEERING

Can you turn LLMs and agents into reliable product capabilities rather than simple API wrappers?


USER INTELLIGENCE

Can you design systems that gradually understand a person from sparse and changing signals?


SYSTEMS THINKING

Can you move from a model to a production system with APIs, data, latency, cost and monitoring?


EVALUATION MINDSET

Can you determine whether the intelligence actually helped the user?


PRODUCT JUDGMENT

Can you decide what the system should do when there is no predefined answer?


SPEED OF EXECUTION

Can you move from idea → prototype → evaluation → production quickly and responsibly?


BUILD WITH US

You will join at a stage where many of the answers do not exist yet. You will not simply implement a model someone else selected; you will help decide how the product learns to understand people.


CAREERS:

Apply with your resume, GitHub, portfolio or shipped work.

https://forms.gle/12YpUSBY2Sqs5xjp8

www.thepersonalabs.com

Read more
Gemba Concepts
at Gemba Concepts
1 candid answer
Vijay Vijay V
Posted by Vijay Vijay V
Bengaluru (Bangalore)
2 - 3 yrs
₹20L - ₹25L / yr
skill iconMachine Learning (ML)
skill iconPython
PyTorch
Convolutional Neural Network (CNN)
Object Detection
+10 more

GEMBA CONCEPTS

Experience: ~3–5 years Type: Full-time

AI/ML Engineer

Location: Bengaluru, India (Hybrid)

About Gemba Concepts

Gemba Concepts is a lean manufacturing and technology consulting firm helping clients across pharma, manufacturing, and logistics

modernize how they operate. We build production systems that sit close to the shop floor — warehouse management, manufacturing

traceability, and an applied AI/ML platform whose flagship use cases are visual quality inspection and predictive maintenance. We’re a

tight engineering team that ships real systems for demanding, often regulated, environments.

The Role

We’re looking for an AI/ML Engineer to take ML capabilities from prototype to production. You’ll own models end-to-end — framing the

problem with stakeholders, building and validating the model, and deploying it as a reliable service that holds up against real-world, messy

industrial data. This is a hands-on building role, not a pure research seat: your work goes into client-facing systems.

What You’ll Do

Build and ship computer vision models for visual quality inspection (defect detection, classification, segmentation) that perform under

real factory lighting, throughput, and edge-case conditions.

Develop predictive maintenance models using sensor/time-series data — anomaly detection, remaining-useful-life estimation, failure

prediction.

Own the full ML lifecycle: data pipelines, feature engineering, training, evaluation, and deployment, with proper versioning and monitoring.

Deploy and serve models in production on Azure (AKS), and keep them healthy — track drift, retraining triggers, and latency.

Integrate LLM-based capabilities (we use the Claude API and self-hosted open models) into delivery and product workflows where they

add leverage.

Collaborate with product, engineering, and domain experts to translate fuzzy operational problems into well-scoped ML solutions — and to

know when ML is not the right answer.

Communicate results and limitations clearly to non-ML stakeholders, including clients.

What We’re Looking For

3–5 years of hands-on experience building and deploying ML models in production (not just notebooks or coursework).

Strong Python and the modern ML stack — PyTorch or TensorFlow, scikit-learn, NumPy/Pandas.

Solid grounding in at least one of: computer vision (CNNs, object detection/segmentation, image preprocessing) or time-series /

anomaly detection.

Practical MLOps experience: containerization (Docker), model serving, experiment tracking, and deploying on a cloud platform — Azure /

Kubernetes (AKS) is a strong plus.

Comfort working with imperfect, real-world data — labeling strategy, class imbalance, data drift, and validation that reflects production

reality.

Good engineering hygiene (Git, testing, code review) and the ability to write code others can build on.

Nice to Have

Experience with industrial / manufacturing data or regulated environments (pharma, 21 CFR Part 11 awareness).

Hands-on LLM integration experience — RAG, prompt engineering, working with APIs or self-hosted models (vLLM, Qwen, etc.).

Edge deployment experience (running CV models on-device / near the line).

Exposure to data pipeline tooling and orchestration.

What You’ll Get

Real ownership of ML systems that go into production for serious clients.

A lean, senior-heavy team where you ship fast and learn across the stack.

Direct exposure to applied AI in manufacturing — a domain where the work has tangible, physical impact

Read more
Gurugram
4 - 8 yrs
₹15L - ₹25L / yr
Generative AI
Agentic AI
skill iconAmazon Web Services (AWS)
skill iconPython

Job Summary

We are looking for an experienced AI/ML Engineer to design, develop, deploy, and maintain machine learning and AI solutions that address complex business problems. The ideal candidate should have strong hands-on experience in Python, Machine Learning, Generative AI, LLMs, and AI/ML deployment, with the ability to work across the complete AI/ML lifecycle.

Key Responsibilities

  • Design, develop, and deploy scalable Machine Learning and AI models for real-world business use cases.
  • Build and optimize ML pipelines covering data preparation, feature engineering, model development, evaluation, and deployment.
  • Develop solutions using Generative AI, Large Language Models (LLMs), NLP, and deep learning.
  • Work with LLMs, prompt engineering, embeddings, vector databases, and Retrieval-Augmented Generation (RAG) architectures.
  • Integrate AI/ML models with enterprise applications and APIs.
  • Fine-tune and evaluate ML/LLM models based on business requirements.
  • Implement MLOps practices for model versioning, deployment, monitoring, and continuous improvement.
  • Collaborate with Data Scientists, Software Engineers, Architects, Product Managers, and business stakeholders.
  • Conduct model performance evaluation, optimization, and troubleshooting.
  • Ensure AI solutions meet requirements around security, scalability, reliability, responsible AI, and data privacy.
  • Stay current with emerging AI/ML technologies, frameworks, and industry best practices.

Required Skills

  • Strong programming experience in Python.
  • Strong understanding of Machine Learning algorithms, statistics, and data structures.
  • Hands-on experience with ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or equivalent.
  • Experience with Generative AI and LLMs such as OpenAI, Azure OpenAI, Claude, Gemini, or open-source models.
  • Strong knowledge of Prompt Engineering, RAG, embeddings, vector databases, and AI agents.
  • Experience with NLP, deep learning, or computer vision is an advantage.
  • Experience developing and consuming REST APIs and microservices.
  • Working knowledge of SQL and NoSQL databases.
  • Experience with cloud platforms such as Azure, AWS, or GCP.
  • Understanding of Docker, Kubernetes, CI/CD, and MLOps.
  • Familiarity with Git and modern software development practices.

Preferred Qualifications

  • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
  • 4 years of relevant experience in AI/ML engineering or a related field.
  • Experience building and deploying production-grade AI/ML solutions.
  • Enterprise application development experience.
  • Experience with Azure OpenAI, AWS Bedrock, Vertex AI, or similar managed AI platforms.
  • Experience with LangChain, LlamaIndex, Semantic Kernel, or comparable AI frameworks is a plus.
  • Experience with AI/ML model monitoring, evaluation, and optimization.

What You Bring

  • Strong problem-solving and analytical skills.
  • Ability to translate business requirements into practical AI/ML solutions.
  • Strong software engineering and debugging capabilities.
  • Ability to work independently as well as collaboratively in a cross-functional environment.
  • Good communication skills with the ability to explain complex AI concepts to technical and non-technical stakeholders.

Keywords

AI Engineer | ML Engineer | Machine Learning | Generative AI | LLM | Python | NLP | Deep Learning | RAG | Prompt Engineering | AI Agents | Azure OpenAI | AWS Bedrock | MLOps | TensorFlow | PyTorch | Scikit-learn | Vector Database | Cloud AI

 

Location: Gurugram

Work mode: Hybrid


Read more
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