Machine Learning (ML)
Deep LearningJob Title: AI/ML Engineer – Voice (2–3 Years)
Location: Bengaluru (On-site)
Employment Type: Full-time
About Impacto Digifin Technologies
Impacto Digifin Technologies enables enterprises to adopt digital transformation through intelligent, AI-powered solutions. Our platforms reduce manual work, improve accuracy, automate complex workflows, and ensure compliance—empowering organizations to operate with speed, clarity, and confidence.
We combine automation where it’s fastest with human oversight where it matters most. This hybrid approach ensures trust, reliability, and measurable efficiency across fintech and enterprise operations.
Role Overview
We are looking for an AI Engineer Voice with strong applied experience in machine learning, deep learning, NLP, GenAI, and full-stack voice AI systems.
This role requires someone who can design, build, deploy, and optimize end-to-end voice AI pipelines, including speech-to-text, text-to-speech, real-time streaming voice interactions, voice-enabled AI applications, and voice-to-LLM integrations.
You will work across core ML/DL systems, voice models, predictive analytics, banking-domain AI applications, and emerging AGI-aligned frameworks. The ideal candidate is an applied engineer with strong fundamentals, the ability to prototype quickly, and the maturity to contribute to R&D when needed.
This role is collaborative, cross-functional, and hands-on.
Key Responsibilities
Voice AI Engineering
- Build end-to-end voice AI systems, including STT, TTS, VAD, audio processing, and conversational voice pipelines.
- Implement real-time voice pipelines involving streaming interactions with LLMs and AI agents.
- Design and integrate voice calling workflows, bi-directional audio streaming, and voice-based user interactions.
- Develop voice-enabled applications, voice chat systems, and voice-to-AI integrations for enterprise workflows.
- Build and optimize audio preprocessing layers (noise reduction, segmentation, normalization)
- Implement voice understanding modules, speech intent extraction, and context tracking.
Machine Learning & Deep Learning
- Build, deploy, and optimize ML and DL models for prediction, classification, and automation use cases.
- Train and fine-tune neural networks for text, speech, and multimodal tasks.
- Build traditional ML systems where needed (statistical, rule-based, hybrid systems).
- Perform feature engineering, model evaluation, retraining, and continuous learning cycles.
NLP, LLMs & GenAI
- Implement NLP pipelines including tokenization, NER, intent, embeddings, and semantic classification.
- Work with LLM architectures for text + voice workflows
- Build GenAI-based workflows and integrate models into production systems.
- Implement RAG pipelines and agent-based systems for complex automation.
Fintech & Banking AI
- Work on AI-driven features related to banking, financial risk, compliance automation, fraud patterns, and customer intelligence.
- Understand fintech data structures and constraints while designing AI models.
Engineering, Deployment & Collaboration
- Deploy models on cloud or on-prem (AWS / Azure / GCP / internal infra).
- Build robust APIs and services for voice and ML-based functionalities.
- Collaborate with data engineers, backend developers, and business teams to deliver end-to-end AI solutions.
- Document systems and contribute to internal knowledge bases and R&D.
Security & Compliance
- Follow fundamental best practices for AI security, access control, and safe data handling.
- Awareness of financial compliance standards (plus, not mandatory).
- Follow internal guidelines on PII, audio data, and model privacy.
Primary Skills (Must-Have)
Core AI
- Machine Learning fundamentals
- Deep Learning architectures
- NLP pipelines and transformers
- LLM usage and integration
- GenAI development
- Voice AI (STT, TTS, VAD, real-time pipelines)
- Audio processing fundamentals
- Model building, tuning, and retraining
- RAG systems
- AI Agents (orchestration, multi-step reasoning)
Voice Engineering
- End-to-end voice application development
- Voice calling & telephony integration (framework-agnostic)
- Realtime STT ↔ LLM ↔ TTS interactive flows
- Voice chat system development
- Voice-to-AI model integration for automation
Fintech/Banking Awareness
- High-level understanding of fintech and banking AI use cases
- Data patterns in core banking analytics (advantageous)
Programming & Engineering
- Python (strong competency)
- Cloud deployment understanding (AWS/Azure/GCP)
- API development
- Data processing & pipeline creation
Secondary Skills (Good to Have)
- MLOps & CI/CD for ML systems
- Vector databases
- Prompt engineering
- Model monitoring & evaluation frameworks
- Microservices experience
- Basic UI integration understanding for voice/chat
- Research reading & benchmarking ability
Qualifications
- 2–3 years of practical experience in AI/ML/DL engineering.
- Bachelor’s/Master’s degree in CS, AI, Data Science, or related fields.
- Proven hands-on experience building ML/DL/voice pipelines.
- Experience in fintech or data-intensive domains preferred.
Soft Skills
- Clear communication and requirement understanding
- Curiosity and research mindset
- Self-driven problem solving
- Ability to collaborate cross-functionally
- Strong ownership and delivery discipline
- Ability to explain complex AI concepts simply
Shubham Vishwakarma
Full Stack Developer - Averlon
About Impacto Digifin Technologies
About
Impacto Digifin Technologies empowers businesses to embrace digital transformation with intelligent, AI-driven solutions. Our platforms simplify document management, data verification, and compliance processes, reducing manual effort, enhancing accuracy, and accelerating results. From fast-growing fintechs to established enterprises, our solutions are designed to adapt to unique operational needs, whether it’s streamlining customer onboarding, automating back-office workflows, or eliminating paperwork bottlenecks.
What sets Impacto Digifin apart is our hybrid approach—leveraging automation for speed while maintaining human oversight where it matters most. This ensures efficiency without compromising trust, enabling organizations to operate with clarity and control. More than just a technology provider, we act as a digital partner, helping teams scale smarter, optimize processes, and transform their operations with confidence.
Candid answers by the company
Impacto Digifin Technologies provides AI-powered solutions that streamline document management, data verification, and compliance for businesses.
Connect with the team
Company social profiles
Similar jobs
🚀 We're Hiring — Backend Developer (3–5 Yrs Exp)
📍 Location: Coimbatore / Remote (India)
⏱ Notice: Immediate
🔧 What You'll Do:
• Build and maintain scalable REST APIs
• Work with Django/Flask on production-grade systems
• Manage databases — PostgreSQL, MySQL, MongoDB
• Implement caching (Redis) & queuing (RabbitMQ/Kafka)
• Deploy & manage services using Docker
• Work on AWS or GCP cloud infrastructure
• Collaborate via Git, CI/CD pipelines
✅ You Should Have:
• 3–5 years of hands-on Python backend experience
• Strong grasp of data structures & design patterns
• Comfort with Linux & version control workflows
• Good communication & team collaboration skills
⭐ Bonus Points:
• Experience with LLM integrations or RAG pipelines
• Familiarity with Vector DBs (Pinecone/ChromaDB)
• Prior startup or fast-paced environment experience
Interested? Drop your resume or refer someone who'd be a great fit! 💼
Location: Mumbai, Maharashtra, India
Sector: Technology, Information & Media
Company Size: 500 - 1,000 Employees
Employment: Full-Time, Permanent
Experience: 10 - 14 Years (Engineering Leadership)
Level: Engineering Manager / Group EM
ABOUT THIS MANDATE :
Recruiting Bond has been exclusively retained by one of India's most prominent and well-established digital platform organisations operating at the intersection of Technology, Information, and Media to identify and place an exceptional Engineering Manager who can lead engineering teams through an enterprise-wide AI adoption and digital transformation agenda.
This is a high-impact, hands-on leadership role at the nexus of people, product, and technology. The organisation is executing one of the most ambitious AI transformation programmes in its sector and this Engineering Manager will be a core driver of that change. You will lead multiple squads, own engineering delivery end-to-end, embed AI tooling and practices into the team's DNA, and shape the engineering culture of tomorrow.
We are seeking leaders who code when it matters, who build systems and teams with equal conviction, and who view AI not as a trend but as a fundamental shift in how great software is built.
THE OPPORTUNITY AT A GLANCE :
AI-First Engineering Culture :
- Own AI adoption across your squads - from LLM tooling integration to automation-first delivery workflows. Make AI a default, not an afterthought.
Hands-On Engineering Leadership :
- Stay close to the code. Lead architecture reviews, unblock engineers, and set the technical bar - not just the management agenda.
People & Org Builder :
- Grow engineers into leaders. Build squads of 615 across functions. Drive hiring, career frameworks, and a culture of psychological safety.
KEY RESPONSIBILITIES :
1. Hands-On Technical Engagement :
- Remain deeply embedded in the technical work participate in design reviews, architecture decisions, and critical code reviews
- Set and uphold the engineering quality bar : performance benchmarks, security standards, test coverage, and release quality
- Provide technical direction on backend platform strategy, API design, service decomposition, and data architecture
- Identify and resolve systemic technical debt and architectural risks across team-owned services
- Unblock engineers by diving into complex problems debugging, pair programming, and system analysis when it matters
- Own key technical decisions in collaboration with Tech Leads and Principal Engineers; balance pragmatism with long-term sustainability
2. AI Adoption, Integration & Transformation (2026 Mandate) :
- Define and execute the team's AI adoption roadmap - from developer tooling to product-facing AI features
- Champion the integration of GenAI tools (GitHub Copilot, Cursor, Claude, ChatGPT) across the full engineering workflow coding, testing, documentation, incident response
- Embed LLM-powered capabilities into the product : recommendation engines, intelligent search, conversational interfaces, content generation, and predictive systems
- Lead evaluation and adoption of AI-assisted SDLC practices : automated code review, AI-generated test suites, intelligent observability, and anomaly detection
- Partner with Data Science and ML Platform teams to productionise ML models with robust MLOps pipelines
- Build team literacy in prompt engineering, RAG (Retrieval-Augmented Generation), and AI agent frameworks
- Create an experimentation culture : run structured AI pilots, measure productivity impact, and scale what works
- Stay ahead of the AI tooling landscape and advise senior leadership on strategic AI investments and engineering implications
3. People Leadership & Team Development :
- Lead, manage, and grow squads of 6 - 15 engineers across seniority levels (L2 through L6 / Junior through Staff)
- Conduct structured 1 : 1s, career growth conversations, and development planning with every direct report
- Design and execute personalised AI upskilling programmes ensure every engineer develops practical AI fluency by end of 2026
- Build and maintain a high-performance team culture : clarity of ownership, accountability, fast feedback loops, and psychological safety
- Drive performance management fairly and rigorously recognise top performers, manage underperformance constructively
- Lead technical hiring end-to-end : define job requirements, conduct bar-raising interviews, and make data-driven hire decisions
- Contribute to engineering career frameworks and level definitions in partnership with the VP / Director of Engineering
4. Engineering Delivery & Execution Excellence :
- Own end-to-end delivery for multiple product squads from planning and scoping through production release and post-launch stability
- Implement and refine agile delivery frameworks (Scrum, Kanban, Shape Up) calibrated to squad needs and product cadence
- Drive predictable delivery : maintain healthy sprint velocity, manage WIP limits, and ensure dependency resolution across teams.
- Establish and own engineering KPIs : DORA metrics (deployment frequency, lead time, MTTR, change failure rate), uptime SLOs, and velocity trends
- Lead incident management : build blameless post-mortem culture, own RCA processes, and drive systemic reliability improvements
- Balance technical debt repayment with feature velocity negotiate prioritisation transparently with Product leadership
5. Strategic Leadership & Cross-Functional Influence :
- Serve as the primary engineering partner for Product, Design, Data, and Business stakeholders translate ambiguity into executable engineering plans
- Participate in quarterly roadmap planning, capacity forecasting, and OKR definition for engineering teams
- Represent engineering in leadership forums articulate technical constraints, risks, and opportunities in business terms
- Contribute to org-wide engineering strategy : platform investments, build-vs-buy decisions, and shared infrastructure priorities
- Build relationships across geographies (Mumbai HQ + distributed teams) to maintain alignment and delivery cohesion
- Act as a culture carrier and ambassador for engineering excellence, innovation, and responsible AI use
AI TRANSFORMATION LEADERSHIP 2026 EXPECTATIONS :
In 2026, Engineering Managers at this organisation are expected to be active architects of AI transformation not passive observers. The following outlines the specific AI leadership expectations for this role :
AI Developer Productivity
- Drive measurable uplift in developer velocity through AI tooling adoption. Target : 30%+ reduction in code review cycle time and 40%+ increase in test coverage automation by Q3 2026.
LLM & GenAI Product Features
- Own delivery of GenAI-powered product capabilities : intelligent content, semantic search, personalisation, and conversational UX in production, at scale.
AI-Augmented Observability
- Implement AI-driven monitoring and anomaly detection pipelines. Reduce MTTR by leveraging predictive alerting, intelligent runbooks, and auto-remediation scripts.
Team AI Fluency :
- Build mandatory AI literacy across all engineering levels.
- Every engineer understands prompt engineering basics, AI ethics guardrails, and responsible AI deployment practices.
Responsible AI Governance :
- Partner with Security, Legal, and Data Privacy to ensure all AI deployments meet compliance standards, bias mitigation requirements, and explainability benchmarks.
TECHNOLOGY STACK & DOMAIN FAMILIARITY REQUIRED :
- Languages: Java/ Go/ Python/ Node.js /PHP /Rust (must be hands-on in at least 2)
- Cloud: AWS / GCP / Azure (multi-cloud exposure strongly preferred)
- AI & GenAI: OpenAI / Anthropic / Gemini APIs /LangChain /LlamaIndex / RAG / Vector DBs / GitHub
- Copilot: Cursor /Hugging Face
- Containers: Docker /Kubernetes /Helm /Service Mesh (Istio / Linkerd)
- Databases: PostgreSQL /MongoDB / Redis / Cassandra / Elasticsearch / Pinecone (Vector DB)
- Messaging: Apache Kafka /RabbitMQ /AWS SQS/SNS /Google Pub/Sub
- MLOps & DataOps: MLflow /Kubeflow / SageMaker / Vertex AI /Airflow /dbt
- Observability: Datadog /Prometheus /Grafana /OpenTelemetry / Jaeger /ELK Stack
- CI/CD & IaC: GitHub Actions ArgoCD / Jenkins / Terraform /Ansible /Backstage (IDP)
QUALIFICATIONS & CANDIDATE PROFILE :
Education :
- B.E. / B.Tech or M.E. / M.Tech from a Tier-I or Tier-II Institution - CS, IS, ECE, AI/ML streams strongly preferred
- Demonstrated engineering depth and leadership impact may complement institution pedigree
Experience :
- 10 to 14 years of progressive engineering experience, with at least 3 years in a formal Engineering Manager or equivalent people-leadership role
- Proven track record of managing and scaling engineering teams (615+ engineers) in a fast-growing SaaS or digital product environment
- Hands-on backend engineering background must be able to read, write, and critique production code
- Direct experience driving AI/ML feature delivery or AI tooling adoption within engineering organisations
- Exposure across start-up, mid-size, and large-scale product organisations, preferred adaptability is a core requirement
- Strong CS fundamentals: distributed systems, algorithms, system design, and software architecture
- Demonstrated career stability minimum of 2 years of average tenure per organisation.
The Ideal Engineering Manager in 2026 :
- Leads with context, not control, empowers engineers while maintaining accountability and quality
- Is fluent in both people language and technical language, switches registers naturally with engineers and executives alike
- Sees AI as a force multiplier for the team, not a threat. Actively experiments with and advocates for AI tooling
- Measures success by team outcomes, not personal output. Takes pride in what the team ships, not what they build alone
- Creates feedback loops obsessively between product and engineering, between seniors and juniors, between metrics and decisions
- Has strong opinions, loosely held, brings conviction to discussions but updates on evidence
- Invests in engineering excellence as seriously as delivery velocity knows that quality and speed are not opposites
WHY THIS ROLE STANDS APART :
AI Transformation at Scale :
- Lead one of the most significant AI adoption programmes in India's digital media sector.
- Our decisions will shape how hundreds of engineers work in 2026 and beyond.
Hands-On & Strategic Balance :
- A rare EM role that actively encourages technical depth.
- Stay close to the code while owning the people agenda - the best of both worlds.
Established Platform, Real Scale :
- 5001,000 engineers, proven product-market fit, and the org maturity to execute.
- This is not a greenfield startup gamble it is a serious company with serious ambition.
Clear Leadership Growth Path :
- A visible, direct path toward Director / VP of Engineering.
- Senior leadership is invested in growing its next generation of technology executives.
Python
Flask
DjangoJob Title: Python Backend / GenAI Engineer (4+ Years)
Job Summary
Looking for a Python Backend Engineer with experience in Generative AI, LangGraph workflows, data engineering, and AI evaluation using Arize AI.
Responsibilities
* Develop backend APIs using Python (FastAPI / Flask / Django)
* Build Generative AI and RAG-based applications
* Design LangGraph / agent workflows
* Create data engineering pipelines (ETL, data processing)
* Implement LLM monitoring and evaluation using Arize AI
* Integrate vector databases and AI services
* Maintain scalable and production-ready backend systems
Required Skills
* 4+ years of Python backend development
* Experience in Generative AI / LLM applications
* Knowledge of LangGraph / LangChain
* Experience in data engineering pipelines
* Familiarity with Arize AI or model evaluation tools
* Understanding of REST APIs, databases, Docker
Good to Have
* Cloud platforms (Azure / AWS )
* Vector databases (FAISS, Pinecone, Azure AI Search)
PythonAbout Corridor Platforms
Corridor Platforms is a leader in next-generation risk decisioning and responsible AI governance, empowering banks and lenders to build transparent, compliant, and data-driven solutions. Our platforms combine advanced analytics, real-time data integration, and GenAI to support complex financial decision workflows for regulated industries.
Role Overview
As a Backend Engineer at Corridor Platforms, you will:
- Architect, develop, and maintain backend components for our Risk Decisioning Platform.
- Build and orchestrate scalable backend services that automate, optimize, and monitor high-value credit and risk decisions in real time.
- Integrate with ORM layers – such as SQLAlchemy – and multi RDBMS solutions (Postgres, MySQL, Oracle, MSSQL, etc) to ensure data integrity, scalability, and compliance.
- Collaborate closely with Product Team, Data Scientists, QA Teams to create extensible APIs, workflow automation, and AI governance features.
- Architect workflows for privacy, auditability, versioned traceability, and role-based access control, ensuring adherence to regulatory frameworks.
- Take ownership from requirements to deployment, seeing your code deliver real impact in the lives of customers and end users.
Technical Skills
- Languages: Python 3.9+, SQL, JavaScript/TypeScript, Angular
- Frameworks: Flask, SQLAlchemy, Celery, Marshmallow, Apache Spark
- Databases: PostgreSQL, Oracle, SQL Server, Redis
- Tools: pytest, Docker, Git, Nx
- Cloud: Experience with AWS, Azure, or GCP preferred
- Monitoring: Familiarity with OpenTelemetry and logging frameworks
Why Join Us?
- Cutting-Edge Tech: Work hands-on with the latest AI, cloud-native workflows, and big data tools—all within a single compliant platform.
- End-to-End Impact: Contribute to mission-critical backend systems, from core data models to live production decision services.
- Innovation at Scale: Engineer solutions that process vast data volumes, helping financial institutions innovate safely and effectively.
- Mission-Driven: Join a passionate team advancing fair, transparent, and compliant risk decisioning at the forefront of fintech and AI governance.
What We’re Looking For
- Proficiency in Python, SQLAlchemy (or similar ORM), and SQL databases.
- Experience developing and maintaining scalable backend services, including API, data orchestration, ML workflows, and workflow automation.
- Solid understanding of data modeling, distributed systems, and backend architecture for regulated environments.
- Curiosity and drive to work at the intersection of AI/ML, fintech, and regulatory technology.
- Experience mentoring and guiding junior developers.
Ready to build backends that shape the future of decision intelligence and responsible AI?
Apply now and become part of the innovation at Corridor Platforms!
Deep LearningAbout the Role
We are looking for a passionate AI Engineer Intern (B.Tech, M.Tech / M.S. or equivalent) with strong foundations in Artificial Intelligence, Computer Vision, and Deep Learning to join our R&D team.
You will help us build and train realistic face-swap and deepfake video models, powering the next generation of AI-driven video synthesis technology.
This is a remote, individual-contributor role offering exposure to cutting-edge AI model development in a startup-like environment.
Key Responsibilities
- Research, implement, and fine-tune face-swap / deepfake architectures (e.g., FaceSwap, SimSwap, DeepFaceLab, LatentSync, Wav2Lip).
- Train and optimize models for realistic facial reenactment and temporal consistency.
- Work with GANs, VAEs, and diffusion models for video synthesis.
- Handle dataset creation, cleaning, and augmentation for face-video tasks.
- Collaborate with the AI core team to deploy trained models in production environments.
- Maintain clean, modular, and reproducible pipelines using Git and experiment-tracking tools.
Required Qualifications
- B.Tech, M.Tech / M.S. (or equivalent) in AI / ML / Computer Vision / Deep Learning.
- Certifications in AI or Deep Learning (DeepLearning.AI, NVIDIA DLI, Coursera, etc.).
- Proficiency in PyTorch or TensorFlow, OpenCV, FFmpeg.
- Understanding of CNNs, Autoencoders, GANs, Diffusion Models.
- Familiarity with datasets like CelebA, VoxCeleb, FFHQ, DFDC, etc.
- Good grasp of data preprocessing, model evaluation, and performance tuning.
Preferred Skills
- Prior hands-on experience with face-swap or lip-sync frameworks.
- Exposure to 3D morphable models, NeRF, motion transfer, or facial landmark tracking.
- Knowledge of multi-GPU training and model optimization.
- Familiarity with Rust / Python backend integration for inference pipelines.
What We Offer
- Work directly on production-grade AI video synthesis systems.
- Remote-first, flexible working hours.
- Mentorship from senior AI researchers and engineers.
- Opportunity to transition into a full-time role upon outstanding performance.
Location: Remote | Stipend: ₹10,000/month | Duration: 3–6 months
Python
Java
Scala
Java
Elastic Search- You will be responsible for design, development and testing of Products
- Contributing in all phases of the development lifecycle
- Writing well designed, testable, efficient code
- Ensure designs are in compliance with specifications
- Prepare and produce releases of software components
- Support continuous improvement by investigating alternatives and technologies and presenting these for architectural review
- Some of the technologies you will be working on: Core Java, Solr, Hadoop, Spark, Elastic search, Clustering, Text Mining, NLP, Mahout and Lucene etc.
JavaMachine Learning (ML)
NodeJS (Node.js)
PHPPython
Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon