Python
Machine Learning (ML)ML Engineer — Test & Learn Platform (3+ Years Experience)
About the Role
We're looking for an ML Engineer to join our Test & Learn Platform team. You'll build
and scale our experimentation and causal inference services — from statistical engines
to API integrations and cloud pipelines — empowering business teams globally to make
data-driven decisions.
What You'll Do
Develop and maintain statistical/ML modules (DID, Synthetic Control, A/B
Testing, Multi-Treatment Effects) in Python
Build and extend FastAPI services and integrate them with our web application
via SDK wrappers
Design and optimize large-scale data pipelines using PySpark, Delta Lake, and
Azure Data Lake
Profile and resolve OOM issues in PySpark jobs — optimize memory allocation,
partitioning, broadcast joins, caching strategies, and Spark configurations
Deploy and manage workloads on Databricks, including job clusters, notebooks,
and Delta Lake tables
Containerize and deploy services using Docker, Kubernetes, and CI/CD pipelines
Ensure code quality and security via SonarCloud, Snyk, and pytest
Collaborate with data scientists and product teams to translate research into
production-ready modules
Must-Have Skills
Python (3.9+) — 3+ years of production experience
PySpark & Spark Internals — strong experience with Spark memory model,
executor tuning, shuffle optimization, and diagnosing/resolving OOM errors
(broadcast thresholds, partition skew, spill-to-disk, GC tuning)
Databricks — hands-on with job orchestration, cluster configuration, notebook
workflows, and Delta Lake optimization (Z-ordering, compaction, caching)
Causal Inference & Experimentation — DID, synthetic control, A/B testing,
hypothesis testing, panel data methods
Statistics/ML Libraries — statsmodels, scikit-learn, scipy, pandas, numpy
API Development — building RESTful services with FastAPI (or similar)
Cloud (Azure) — Azure Storage, Azure ML, Data Lake
Docker & Kubernetes — containerization and orchestration for ML workloads
Testing — writing robust unit/integration tests with pytest
Nice-to-Have
Experience with Celery/Redis for async task orchestration
Familiarity with Polars, PyArrow, or SQLAlchemy
Background in econometrics or experimental design
Spark UI profiling and performance benchmarking
CI/CD tooling (SonarCloud, Snyk, GitHub Actions)
What Sets You Apart
You can look at a Spark execution plan and pinpoint why a job is OOM-ing
You think in modules — clean separation of data processing, inference, and post-
processing
You can go from a Jupyter notebook prototype to a production-grade, testable
service
You're comfortable with both statistical rigor and software engineering best
practices
Shubham Vishwakarma
Full Stack Developer - Averlon
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About MyOperator
MyOperator is a Business AI Operator and category leader that unifies WhatsApp, Calls, and AI-powered chatbots & voice bots into one intelligent business communication platform.Unlike fragmented communication tools, MyOperator combines automation, intelligence, and workflow integration to help businesses run WhatsApp campaigns, manage calls, deploy AI chatbots, and track performance — all from a single no-code platform.Trusted by 12,000+ brands including Amazon, Domino’s, Apollo, and Razor-pay, MyOperator enables faster responses, higher resolution rates, and scalable customer engagement
Role Summary
We’re hiring a Front Deployed Engineer (FDE)—a customer-facing, field-deployed engineer who owns the end-to-end delivery of AI bots/agents.
This role is “frontline”: you’ll work directly with customers (often onsite), translate business reality into bot workflows, do prompt engineering + knowledge grounding, ship deployments, and iterate until it works reliably in production.
Think: solutions engineer + implementation engineer + prompt engineer, with a strong bias for execution.
Responsibilities
Requirement Discovery & Stakeholder Interaction
- Join customer calls alongside Sales and Revenue teams.
- Ask targeted questions to understand business objectives, user journeys, automation expectations, and edge cases.
- Identify data sources (CRM, APIs, Excel, SharePoint, etc.) required for the solution.
- Act as the AI subject-matter expert during client discussions.
Use Case & Solution Documentation
- Convert discussions into clear, structured use case documents, including:
- Problem statement & goals.
- Current vs. proposed conversational flows.
- Chatbot conversation logic, integrations, and dependencies.
- Assumptions, limitations, and success criteria.
Customer Delivery Ownership
Own deployment of AI bots for customer use-cases (lead qualification, support, booking, etc.). Run workshops to capture processes, FAQs, edge cases, and success metrics. Drive the go-live process: requirements through monitoring and improvement.
Prompt Engineering & Conversation Design
Craft prompts, tool instructions, guardrails, fallbacks, and escalation policies for stable behavior. Build structured conversational flows: intents, entities, routing, handoff, and compliant responses. Create reusable prompt patterns and "prompt packs."
Testing, Debugging & Iteration
Analyze logs to find failure modes (misclassification, hallucination, poor handling). Create test sets ("golden conversations"), run regressions, and measure improvements. Coordinate with Product/Engineering for platform needs.
Integrations & Technical Coordination
Integrate bots with APIs/webhooks (CRM, ticketing, internal tools) to complete workflows. Troubleshoot production issues and coordinate fixes/root-cause analysis.
What Success Looks Like
- Customer bots go live quickly and show high containment + high task completion with low escalation.
- You can diagnose failures from transcripts/logs and fix them with prompt/workflow/knowledge changes.
- Customers trust you as the “AI delivery owner”—clear communication, realistic timelines, crisp execution.
Requirements (Must Have)
- 2–5 years in customer-facing delivery roles: implementation, solutions engineering, customer success engineering, or similar.
- Hands-on comfort with LLMs and prompt engineering (structured outputs, guardrails, tool use, iteration).
- Strong communication: workshops, requirement capture, crisp documentation, stakeholder management.
- Technical fluency: APIs/webhooks concepts, JSON, debugging logs, basic integration troubleshooting.
- Willingness to be front deployed (customer calls/visits as needed).
Good to Have (Nice to Have)
- Experience with chatbots/voicebots, IVR, WhatsApp automation, conversational AI platforms with at least a couple of projects.
- Understanding of metrics like containment, resolution rate, response latency, CSAT drivers.
- Prior SaaS onboarding/delivery experience in mid-market or enterprises.
Working Style & Traits We Value
- High agency: you don’t wait for perfect specs—you create clarity and ship.
- Customer empathy + engineering discipline.
- Strong bias for iteration: deploy → learn → improve.
- Calm under ambiguity (real customer environments are chaotic by default).
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Metrics & Reporting:
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Analyzes functional and technical requirements and translate them into manual test scenarios.
Communicate with AQA Engineers to make our e2e-scenarios automated
Participate in scrum team activities and be the voice of quality in your team
Collaborate with other teams, product office, support team and release managers.
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2+ years as a QA Engineer/Tester (Relevant experience including salesforce)
Knowledge of the Agile SDLC utilizing JIRA
Experience creating/updating and maintaining manual test cases, test plans, and test suites
Experience with test case management tools as well as maintaining test cases outside of tools
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Experience troubleshooting and reproducing issues and defects
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Will be great to see:
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Experience with SQL and query writing
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· Design and implementation of low-latency, high-availability, and performant applications.
· Implementation of security and data protection.
· Thorough understanding of relational databases such as MySQL or similar technologies.
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Required Skills:
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· strong hold on Node.js and frameworks.
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Years of Experience: 3 – 7 Years
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Job Location: Jaipur
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- Strong experience with oracle functions, procedures, triggers, packages & performance tuning,
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- Exceptional design and architectural skills
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JavascriptImmediate Openings for Dot Net Developer
Experience: 2 - 4years
Job Profile :-
- Good knowledge of .Net concepts & MVC
- Strong programming and troubleshooting skills using C#, ASP.Net, and MS SQL 2012/2019
- Good experience of working on JavaScript and jQuery
- Good understanding of Web API concepts
Job Description :
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- Member will be engaged in coding, unit testing, and build readiness activities related to assigned tasks.
- Member is expected to adhere to defined coding standards and guidelines. Also, the member will have to work in collaboration with other team members for success of the assigned projects.
Qualification:- B.tech / BCA/MCA
Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon