Sr Machine Learning Engineer at TrumetricAI · Bengaluru (Bangalore) · 6 - 10 years · ₹15L - ₹30L / yr · Bootstrapped · Posted 6 Jan 2026
Deep Learning
Machine Learning (ML)
Data ScienceSenior Machine Learning Engineer
About the Role
We are looking for a Senior Machine Learning Engineer who can take business problems, design appropriate machine learning solutions, and make them work reliably in production environments.
This role is ideal for someone who not only understands machine learning models, but also knows when and how ML should be applied, what trade-offs to make, and how to take ownership from problem understanding to production deployment.
Beyond technical skills, we need someone who can lead a team of ML Engineers, design end-to-end ML solutions, and clearly communicate decisions and outcomes to both engineering teams and business stakeholders. If you enjoy solving real problems, making pragmatic decisions, and owning outcomes from idea to deployment, this role is for you.
What You’ll Be Doing
Building and Deploying ML Models
- Design, build, evaluate, deploy, and monitor machine learning models for real production use cases.
- Take ownership of how a problem is approached, including deciding whether ML is the right solution and what type of ML approach fits the problem.
- Ensure scalability, reliability, and efficiency of ML pipelines across cloud and on-prem environments.
- Work with data engineers to design and validate data pipelines that feed ML systems.
- Optimize solutions for accuracy, performance, cost, and maintainability, not just model metrics.
Leading and Architecting ML Solutions
- Lead a team of ML Engineers, providing technical direction, mentorship, and review of ML approaches.
- Architect ML solutions that integrate seamlessly with business applications and existing systems.
- Ensure models and solutions are explainable, auditable, and aligned with business goals.
- Drive best practices in MLOps, including CI/CD, model monitoring, retraining strategies, and operational readiness.
- Set clear standards for how ML problems are framed, solved, and delivered within the team.
Collaborating and Communicating
- Work closely with business stakeholders to understand problem statements, constraints, and success criteria.
- Translate business problems into clear ML objectives, inputs, and expected outputs.
- Collaborate with software engineers, data engineers, platform engineers, and product managers to integrate ML solutions into production systems.
- Present ML decisions, trade-offs, and outcomes to non-technical stakeholders in a simple and understandable way.
What We’re Looking For
Machine Learning Expertise
- Strong understanding of supervised and unsupervised learning, deep learning, NLP techniques, and large language models (LLMs).
- Experience choosing appropriate modeling approaches based on the problem, available data, and business constraints.
- Experience training, fine-tuning, and deploying ML and LLM models for real-world use cases.
- Proficiency in common ML frameworks such as TensorFlow, PyTorch, Scikit-learn, etc.
Production and Cloud Deployment
- Hands-on experience deploying and running ML systems in production environments on AWS, GCP, or Azure.
- Good understanding of MLOps practices, including CI/CD for ML models, monitoring, and retraining workflows.
- Experience with Docker, Kubernetes, or serverless architectures is a plus.
- Ability to think beyond deployment and consider operational reliability and long-term maintenance.
Data Handling
- Strong programming skills in Python.
- Proficiency in SQL and working with large-scale datasets.
- Ability to reason about data quality, data limitations, and how they impact ML outcomes.
- Familiarity with distributed computing frameworks like Spark or Dask is a plus.
Leadership and Communication
- Ability to lead and mentor ML Engineers and work effectively across teams.
- Strong communication skills to explain ML concepts, decisions, and limitations to business teams.
- Comfortable taking ownership and making decisions in ambiguous problem spaces.
- Passion for staying updated with advancements in ML and AI, with a practical mindset toward adoption.
Experience Needed
- 6+ years of experience in machine learning engineering or related roles.
- Proven experience designing, selecting, and deploying ML solutions used in production.
- Experience managing ML systems after deployment, including monitoring and iteration.
- Proven track record of working in cross-functional teams and leading ML initiatives.
Shubham Vishwakarma
Full Stack Developer - Averlon
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PostgreSQLABOUT
The Persona Labs is building a new kind of social platform focused on something most social products do not explicitly optimize for: helping people become real friends.
We want to help people discover interesting people around them, find meaningful common ground, start low-pressure interactions, continue promising conversations, create shared experiences, and ultimately build real-life friendships.
DISCOVER → CURIOSITY → COMPATIBILITY → INTERACTION → UNDERSTAND → IRL EXPERIENCE → FRIENDSHIP
THE AI LAYER - COMPANION INTELLIGENCE
Alongside the platform, we are building a proactive personal AI companion that learns about the user and helps them navigate this journey through personalized recommendations, suggestions, reminders, conversations, and experiences.
THE OPPORTUNITY
We are looking for a Founding ML Engineer to build the intelligence layer of the platform from the ground up. This is a 0→1 Applied AI / ML role where you will work directly with the founder and Product Engineer to turn ambiguous problems around users, relationships, recommendations and personal intelligence into working systems.
You will be expected to:
Understand the problem → identify the signals → design the intelligence system → prototype → evaluate → deploy → learn → improve.
WHAT YOU WILL BUILD & OWN
USER INTELLIGENCE
User representations, behavioural models, interests, preferences, contextual signals, and evolving understanding of the user. MEMORY Short- and long-term memory, episodic/preference/relationship memory, retrieval, relevance and updating.
RECOMMENDATION & MATCHING
People discovery, compatibility, activity/experience recommendations, and personalized ranking.
INTENT & INTEREST
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RANKING
Decide what should appear first across potentially thousands of relevant people, activities or experiences.
CONTENT INTELLIGENCE
Classification, toxicity, spam, policy signals, quality, relevance, and semantic understanding.
RELATIONSHIP INTELLIGENCE
Reciprocity, interaction health, shared interests, progression, declining engagement and shared activity.
NEXT-BEST-ACTION
Determine the most useful action now: show a person, suggest a question, recommend an activity, reconnect, or do nothing.
TRUST / SAFETY INTELLIGENCE
Fake-account signals, spam, abuse, behavioural anomalies, risky interactions and moderation assistance.
COMPANION INTELLIGENCE
Use signals and outputs to help the companion decide what to say, suggest, recommend or not do.
WHAT YOUR DAY-TO-DAY LOOKS LIKE
• Translate ambiguous product problems into ML/AI system designs.
• Build models and intelligence pipelines using behavioural, relational and contextual signals.
• Develop recommendation, matching and personalization systems.
• Design memory and retrieval systems that help the companion understand the user over time.
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https://forms.gle/12YpUSBY2Sqs5xjp8
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Company: Timble Technologies Pvt. Ltd
Location: Gurugram (Hybrid)
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Timble Glance is a high-growth AI RegTech and B2B SaaS company catering to top-tier BFSI and enterprise clients. We build cutting-edge systems powering 30+ high-scale APIs for digital identity verification, fraud detection, document intelligence, and compliance automation.
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· Collaboration & Ownership: Translate complex business requirements into technical roadmaps, conduct rigorous code reviews, and mentor junior engineers.
Required Qualifications & Skills
· Education: B.Tech / M.Tech in Computer Science, AI/ML, Mathematics, or a related field—Tier-1 institutes (IIT, IIIT, NIT) strongly preferred.
· Experience: 2+ years of hands-on experience developing, deploying, and maintaining ML/Deep Learning or GenAI models in production environments.
· GenAI & NLP Stack: Hands-on experience with LLMs, embeddings, RAG architectures, and frameworks such as LangChain, LlamaIndex, or Hugging Face.
· Deep Learning Frameworks: Strong proficiency in PyTorch or TensorFlow, with deep knowledge of transformer architectures and modern NLP/CV models.
· Software & Data Engineering: Expert-level Python skills (pytest, Git, OOP, asynchronous programming), solid SQL proficiency, and familiarity with data workflows.
· Deployment & Cloud: Practical exposure to cloud platforms (AWS/GCP), containerization (Docker), API frameworks (FastAPI/Flask), and basic orchestration (Kubernetes).
Preferred Qualifications
· Prior domain experience in Fintech, RegTech, Identity Verification (KYC/AML), Fraud Intelligence, or B2B SaaS.
· Experience optimizing models for low latency and inference cost (e.g., ONNX, TensorRT, model quantization).
· Familiarity with workflow orchestrators such as Airflow, Prefect, or Kubeflow.
Python
DjangoMust of Skills/Experience
• System Design
• Python
• TensorFlow
• Google ADK or Lang Graph
• Lang Chain , Lang Graph
• Spark
• Agentic AI Design
• ML Ops
• MCP (client and server)
• FastAPI
• Doc Factory
• RAG
• Golang
• LLMs – Gemini, Open AI
• NLP
• Dev Assistant - AI based code - generation
(Qwen or Claude or Copilot)
• CI/CD
• Good in oral and written communication,
collaboration and be a team player
Good to have skills
• DevOps with K8
• Scripting
• Java
• REST API
• UV
• ReACT
• DocFactory
• Unix
Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon