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Senior AI/ML Engineer Conversational AI & Voice Platform (BFSI) Company: LeadFlowNex AI Location: Remote / New Delhi Job Type: Full-time Experience: 5+ years About LeadFlowNex AI LeadFlowNex AI is building an advanced Conversational AI Voice Platform for BFSI, designed to automate high-volume outbound customer conversations for banking, lending, insurance, and financial services. Our platform enables AI voice agents to communicate naturally with customers, understand conversations, collect information, qualify leads, perform eligibility assessment, and trigger business workflows in real time. We already have a complete software development team covering backend, frontend, integrations, and product engineering. We are now looking for a Senior AI/ML Engineer to own the AI/ML layer of the platform. This is a core technical role. You will be responsible for building, training, fine-tuning, evaluating, and deploying the AI models that power our conversational voice platform. What You Will Own1. AI/ML Model Development Design and develop custom AI/ML models for our platform.Train, fine-tune, evaluate, and optimize machine-learning and deep-learning models.Work with our existing datasets to build production-ready models.Perform data preprocessing, feature engineering, labeling validation, and dataset optimization.Select appropriate architectures and approaches based on business and technical requirements.Build reproducible training and evaluation pipelines.2. Conversational AI & NLP Develop conversational intelligence for AI voice agents.Build intent classification, entity extraction, semantic understanding, and conversation-state models.Improve understanding of Hindi, English, Hinglish, and other Indian languages.Work with LLMs and open-source models for conversational AI.Implement prompt engineering, structured outputs, RAG, tool/function calling, and fine-tuning where appropriate.Develop methods to reduce hallucination and improve factual consistency.3. Voice AI Work with real-time Speech-to-Text (STT), Text-to-Speech (TTS), VAD, turn detection, and streaming speech pipelines.Integrate and optimize technologies such as Whisper, Deepgram, Google Speech-to-Text, Azure Speech, ElevenLabs, or equivalent platforms.Improve latency, accuracy, interruption handling, and naturalness of AI conversations.Work closely with our existing engineering team on telephony and real-time communication integration.4. AI Decisioning & Predictive Models Build models for eligibility, lead qualification, propensity, scoring, ranking, and routing.Develop predictive models using our existing BFSI datasets.Combine machine-learning predictions with deterministic business rules.Build real-time decision-support systems for loan, insurance, and credit-card workflows.Monitor model performance and continuously improve prediction quality.5. Model Evaluation Design and maintain rigorous evaluation frameworks for: Model accuracyPrecision, recall, F1-scoreIntent recognitionEntity extractionSTT accuracy / Word Error RateConversation success rateResponse latencyHallucination/error rateLead qualification rateBusiness conversion metricsModel driftThe objective is not just to build models that perform well in experiments, but to build AI that performs reliably in real customer conversations at production scale. 6. Production AI & MLOps You will work with our existing development and DevOps team to: Deploy trained models into production.Build model inference APIs and services.Optimize inference latency and infrastructure cost.Implement model versioning and monitoring.Establish retraining and continuous-improvement pipelines.Support high-concurrency real-time AI workloads.Work with AWS/cloud infrastructure, Docker, CI/CD, and monitoring systems.What We Already Have You will not be joining as a one-person development team. We already have: Python Full-Stack DevelopersBackend developmentFrontend developmentProduct engineeringAPI/integration developmentDatabase infrastructureDevelopment supportExisting project datasetsProduct/project requirementsYour primary responsibility will be the AI/ML layer. You will work closely with the existing engineering team to integrate your models into the production platform. What We're Looking ForRequired 5+ years of professional experience in AI/ML Engineering, Machine Learning, Data Science, or a closely related field.Strong hands-on experience with Python.Proven experience training and deploying ML/DL models.Experience with PyTorch, TensorFlow, Scikit-learn, or equivalent frameworks.Strong understanding of NLP and modern AI architectures.Practical experience with LLMs and Generative AI.Experience with model fine-tuning and/or transfer learning.Strong understanding of model evaluation and experimentation.Experience taking models from development to production.Mandatory Model-Building Experience This is not an AI API integration role. Candidates must have hands-on experience with: Data Training .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.