Account Executive

Responsibilities:

• Design and develop scalable web application architecture

• Build and maintain robust services and RESTful APIs

• Develop reusable, secure, and high-quality code

• Optimize applications for performance, speed, and scalability

• Implement security and data protection best practices

• Translate UI/UX wireframes into responsive user interfaces

• Integrate front-end and back-end components seamlessly

Skills & Qualifications:

• Software Engineering degree with 3–5 years of experience

• Strong expertise in JavaScript and Node.js

• Good working knowledge of Angular

• Experience with testing frameworks (Jest, Mocha)

• Hands-on experience with Docker and containerization

• Exposure to Cloud platforms (AWS / Azure)

• Understanding of CI/CD pipelines and DevOps practices

• Experience building cloud-based SaaS applications

• Ability to work effectively in cross-functional teams

JobTitle : Mission Control Software Engineer – Java Backend

Experience : 4+ Years

Location : Bengaluru, Karnataka (On-site)

Employment Type : Full-time

Industry : Space Technology

🧠 Role Overview :

As a Mission Control Software Engineer – Java Backend, you will be a founding member of the Mission Control Software team. You will design and develop the backend core of a distributed, high-reliability system responsible for streaming, processing, and routing satellite data.

This role offers high ownership and the opportunity to directly influence system architecture as it evolves from a single-node deployment to a scalable multi-node system.

🛠️ Tech Stack :

Java 11+, gRPC, REST APIs, PostgreSQL, Docker, Linux, WebSockets, Git, CI/CD

⚙️ Key Responsibilities :

• Design, develop, and maintain backend services in Java (11+) for data ingestion, processing, and control workflows
• Build and enhance gRPC and REST APIs for internal and external system integrations
• Develop modular plugins and middleware for routing, persistence, and automation
• Work with PostgreSQL and other databases for structured and time-series data
• Collaborate with frontend teams to support real-time dashboards via WebSockets/event streaming
• Ensure system scalability, reliability, and observability through logging, metrics, and testing
• Contribute to containerized deployments (Docker) and CI/CD pipelines
• Write clean, maintainable, and well-documented code with strong test coverage

🧩 Required Skills & Experience :

• 4+ years of experience in backend or distributed systems development.
• Strong proficiency in Java (11+), including concurrency and networking concepts.
• Experience with frameworks like Spring Boot, Vert.x, or similar.
• Hands-on experience with gRPC and REST API design.
• Strong knowledge of SQL databases (PostgreSQL preferred) and schema design.
• Experience working in Linux environments, with Git and Docker.
• Familiarity with event-driven systems / messaging queues (Kafka, RabbitMQ, MQTT, etc.).
• Ability to design scalable architectures and write automated tests.

⭐ Good to Have :

• Experience with real-time systems, telemetry, IoT, or satellite systems
• Exposure to distributed systems / microservices / high-availability architectures
• Working knowledge of Python for scripting or integrations
• Experience with monitoring tools like Prometheus and Grafana
• Interest in hardware-software integrated systems

The Role

You own AI systems end to end. From the speech-to-text models that turn audio into text, to the diarization that separates and identifies speakers, to the agentic layer that turns conversation into memory and action, to the observability and evaluation that keep all of it honest in production. This is a wide role by design. You will own model selection, serving, and production reliability. If you want to tune one model and ignore the system around it, this is not the role.

What You Will Own

•     Speech-to-text. Evaluate, integrate, and optimize STT models across cloud and self-hosted. Drive accuracy and cost trade-offs with ground-truth metrics.

•     Speaker diarization and identification. Push accuracy on hard, real-world, multi-speaker audio.

•     Agentic AI. Build the memory and retrieval pipeline, LLM orchestration, and the agent workflows that sit on top of captured conversation.

•     Model serving and infrastructure. Stand up and optimize self-hosted serving (vLLM, Triton class). Own latency, throughput, and cost per user.

Observability

An always-on wearable means models run in production every second, on messy real-world audio. You own the visibility into that.

•     Instrument the full audio-to-memory pipeline: STT, diarization, retrieval, and LLM calls.

•     Define and track model-quality SLOs in production: transcription drift, diarization error over time, retrieval relevance, latency, throughput, and cost per user.

•     Build dashboards and alerting so model degradation is caught before users feel it.

•     Trace failures across a distributed, always-on system using metrics, logs, and traces.

•     Close the loop. Production signals feed back into evaluation and model selection.

Evaluation

We do not ship what we cannot measure. You own the systems that prove a model is actually better, not just newer.

•     Build and own ground-truth evaluation harnesses for every model in the stack.

•     Measure with real metrics: WER for transcription, DER for diarization, Recall and F1 for retrieval and speaker identification.

•     Build and maintain labeled benchmark datasets that reflect real, messy, multi-speaker audio.

•     Run regression and A/B evaluations on every model swap, prompt change, or pipeline update. Nothing ships on a vibe.

•     Reject anecdotal proxies, single confidence scores, and cherry-picked examples as evidence of quality.

What We Are Looking For

•     3 to 5 years as an AI/ML engineer with production systems behind you. Engineering and production experience is non-negotiable.

•     Depth across the modern AI stack: LLMs, speech models, vector retrieval, model serving.

•     Strong software engineering. You write code that ships and survives contact with real users.

•     Fluency in Python and the production ML ecosystem.

•     Comfort with cloud infrastructure (GCP a plus) and containerized deployment on Kubernetes.

•     A working command of observability and evaluation. You measure first and trust metrics over intuition.

•     First-principles reasoning and metric discipline.

Nice to Have

•     Research background or publications. A strong signal, not a substitute for production work.

•     Audio and speech ML experience (STT, diarization, voice).

•     Experience self-hosting and optimizing open models.

•     Experience with LLM gateway and agent orchestration patterns.

•     Experience building eval harnesses or production model-monitoring systems.

Requirements

Agentic work is must. Audio is good to have

. Self hosting models is a must

 Experience with LLM gateway and agent orchestration is a must have

AuxoAI is seeking a Senior Applied Scientist to design and deploy structured knowledge systems that enable reliable, schema-grounded AI and agent reasoning.

This role sits at the intersection of large language models, knowledge graphs, semantic architectures, and hybrid retrieval systems. The ideal candidate will build systems that transform unstructured data into structured knowledge representations, enforce semantic constraints, and enable hybrid symbolic–neural reasoning in production environments.

You will play a key role in designing scalable semantic infrastructures that support advanced AI use cases such as GraphRAG pipelines, structured extraction, and agent reasoning workflows.

You will work on problems where existing architectures may not be sufficient and will experiment with new approaches that combine machine learning, knowledge graphs, semantic constraints, and classical AI techniques to build reliable, production-grade systems.

Location - Mumbai/Bangalore/Hyderabad/Gurgaon (Hybrid - 3 Days a week in Office)

Responsibilities:

• Design schema-guided information extraction systems using zero-shot and few-shot structured prompting, constrained decoding approaches such as JSON schema enforcement or grammar-based decoding, and function-calling or tool-driven extraction techniques.
• Develop recursive or multi-stage extraction pipelines capable of handling nested entities, hierarchical structures, and cross-document relationships.
• Build ontology-driven systems using frameworks such as LinkML, OWL, SHACL, or similar schema modeling tools, and implement knowledge representations using RDF triples or labeled property graphs.
• Design and optimize entity resolution algorithms using techniques such as blocking strategies, embedding similarity, and rule-based matching.
• Develop ontology alignment techniques and graph embedding models such as Node2Vec or TransE-style approaches where appropriate.
• Design hybrid retrieval architectures combining dense vector retrieval, sparse retrieval techniques, and graph traversal algorithms such as BFS, DFS, path ranking, and neighborhood expansion.
• Build validation systems that enforce schema conformance, detect semantic inconsistencies, and reduce hallucinated or invalid structured outputs.
• Integrate structured knowledge systems into GraphRAG pipelines, agent planning frameworks, and tool-selection workflows.
• Deliver production-grade semantic systems with clear targets for latency, scalability, reliability, and data integrity.

Requirements

• 5+ years of experience building production AI or machine learning systems.
• Strong experience designing and implementing knowledge graphs or ontology-driven architectures.
• Hands-on experience implementing structured extraction techniques, including grammar-constrained decoding, JSON schema enforcement, or AST-style parsing approaches.
• Experience building entity resolution systems beyond simple embedding similarity methods.
• Experience working with graph query languages such as SPARQL or Cypher and optimizing graph query performance.
• Familiarity with RDF, OWL, or property graph data models and semantic data architectures.
• Strong Python engineering skills, with emphasis on data validation, schema integrity, and system reliability.
• Experience designing hybrid symbolic and neural AI systems.

Nice to Have:

• Experience implementing graph algorithms such as PageRank, community detection, or shortest-path algorithms for reasoning chains.
• Experience building graph-enhanced retrieval systems such as GraphRAG.
• Experience designing compositional semantic extraction pipelines.
• Experience implementing reasoning engines or rule-based inference systems.
• Experience benchmarking and evaluating structural extraction accuracy and consistency.

Job Title: CRM Executive

Location: Thane (Majiwada)

Age Requirement: Up to 30 Years

Experience: 1–2 years (Pharma industry – Cardio-Diabetic segment preferred)

Responsibilities:

• Manage and maintain customer databases, ensuring accuracy and completeness.
• Coordinate with sales and marketing teams to support CRM activities.
• Generate and analyze reports using MS Excel (Pivot, VLOOKUP, etc.).
• Ensure timely communication with clients and internal teams.
• Support day-to-day CRM operations to drive efficiency and client satisfaction.

Key Skills:

• Strong communication & coordination skills.
• Proficiency in MS Excel and data management.
• Pharma experience in Cardio-Diabetic segment is mandatory.

Note: This is a top priority position – immediate joiners will be preferred.

Should be good if experience . else intern are most welcome first. unlimited incentives will be provided

About Us:

We’re looking to hire someone to help scale Machine Learning and NLP efforts at Episource. You’ll work with the team that develops the models powering Episource’s product focused on NLP driven medical coding. Some of the problems include improving our ICD code recommendations , clinical named entity recognition and information extraction from clinical notes.

This is a role for highly technical engineers who combine outstanding oral and written communication skills, and the ability to code up prototypes and productionalize using a large range of tools, frameworks, and languages. Most importantly they need to have the ability to autonomously plan and organize their work assignments based on high-level team goals.

What you will do at Episource:

You will be responsible for setting an agenda to develop and build machine learning platforms that positively impact the business, working with partners across the company including operations and engineering. You will be working closely with the machine learning team to design and implement back end components and services. You will be evaluating new technologies, enhancing the applications, and providing continuous improvements to produce high quality software.

Required Skills:

• Strong background in analytics, BI or data science deployments is preferable with 2-6 years of experience

• Knowledge of React/Vue, HTML, CSS

• Experience building and consuming APIs 

• Experience with MySQL, MongoDB and MEAN stack 

• Knowledge and experience with serverless architectures is a plus

• Hands-on experience with AWS or any major cloud service provider for deploying solutions

• Experience with Docker or Kubernetes in deploying solutions on the cloud

• Hands-on experience Python, Apache Spark & Big Data platforms to manipulate large-scale structured and unstructured datasets.

• Fluent in data fundamentals: SQL, data manipulation using a procedural language, statistics, experimentation, and modeling

• Budget max $70/hr
  6+ years of working on techno-functional components.

• Experience in Kronos application versions.

• Experience in configuring Overtime, Grace Period Calculation rules.

• Experience in configuring waiting period rules for vacation and sick codes for hires/rehires.

• Experience in building interfaces within Kronos architecture (inbound/outbound)

• Experience in building reports.

• Demonstrated strong analytical abilities.

• Mandatory in Kronos Interface development and troubleshooting, Application security roles, and workflow approvals mandatory.