113 Lora Jobs - Page 5

Job Description: Graduate degree in a quantitative field (CS, statistics, applied mathematics, machine learning, or related discipline) Good programming skills in Python with strong working knowledge of Python's numerical, data analysis, or AI frameworks such as NumPy, Pandas, Scikit-learn, etc. Experience with LMs (Llama (1/2/3), T5, Falcon, Langchain or framework similar like Langchain) Candidate must be aware of entire evolution history of NLP (Traditional Language Models to Modern Large Language Models), training data creation, training set-up and finetuning Candidate must be comfortable interpreting research papers and architecture diagrams of Language Models Candidate must be comfortable with LORA, RAG, Instruct fine-tuning, Quantization, etc. Predictive modelling experience in Python (Time Series/ Multivariable/ Causal) Experience applying various machine learning techniques and understanding the key parameters that affect their performance Experience of building systems that capture and utilize large data sets to quantify performance via metrics or KPIs Excellent verbal and written communication Comfortable working in a dynamic, fast-paced, innovative environment with several ongoing concurrent projects. Roles & Responsibilities: Lead a team of Data Engineers, Analysts and Data scientists to carry out following activities: Connect with internal / external POC to understand the business requirements Coordinate with right POC to gather all relevant data artifacts, anecdotes, and hypothesis Create project plan and sprints for milestones / deliverables Spin VM, create and optimize clusters for Data Science workflows Create data pipelines to ingest data effectively Assure the quality of data with proactive checks and resolve the gaps Carry out EDA, Feature Engineering & Define performance metrics prior to run relevant ML/DL algorithms Research whether similar solutions have been already developed before building ML models Create optimized data models to query relevant data efficiently Run relevant ML / DL algorithms for business goal seek Optimize and validate these ML / DL models to scale Create light applications, simulators, and scenario builders to help business consume the end outputs Create test cases and test the codes pre-production for possible bugs and resolve these bugs proactively Integrate and operationalize the models in client ecosystem Document project artifacts and log failures and exceptions. Measure, articulate impact of DS projects on business metrics and finetune the workflow based on feedbacks Required Skills g, Pyspark, Python, SQL, Supervised ML, Transformer Models, Transformers, Unsupervised ML, GenAi

Dear Candidate, We are hiring an IoT Engineer to build and deploy connected devices and edge computing solutions. Ideal for engineers with a strong background in hardware-software integration. Key Responsibilities: Design and develop IoT device software and cloud integrations Implement communication protocols (MQTT, CoAP, BLE) Ensure security, performance, and scalability of IoT ecosystems Work with sensors, gateways, and embedded platforms Required Skills & Qualifications: Proficiency in C/C++, Python, or JavaScript Experience with IoT platforms (AWS IoT, Azure IoT Hub, Google IoT Core) Familiarity with edge computing and real-time systems Bonus: Knowledge of LPWAN, Zigbee, or industrial IoT (IIoT) Soft Skills: Strong troubleshooting and problem-solving skills. Ability to work independently and in a team. Excellent communication and documentation skills. Note: If interested, please share your updated resume and preferred time for a discussion. If shortlisted, our HR team will contact you. Kandi Srinivasa Reddy Delivery Manager Integra Technologies

Job Summary: As the Embedded Engineering Manager, you will lead the development and delivery of embedded firmware and hardware solutions for our IoT products. You will work closely with cross-functional teams including hardware, cloud, mobile, and product management to drive high-quality, scalable, and secure embedded solutions from concept through production. Key Responsibilities: Lead a team of embedded software and firmware engineers through the full product lifecycle. Architect, design, and review embedded system software and firmware for IoT devices. Collaborate with hardware engineers to integrate and validate system-level performance. Ensure secure, scalable, and power-efficient designs in accordance with IoT best practices. Define project roadmaps, allocate resources, and manage timelines and deliverables. Drive best practices for coding standards, testing, CI/CD, and code reviews. Identify and mitigate technical risks and resolve complex engineering challenges. Work closely with QA to develop and implement test strategies (unit, integration, system). Stay abreast of industry trends and new technologies to influence design decisions. Mentor, coach, and grow the embedded engineering team. Qualifications: Required: Bachelors or master’s degree in electrical engineering, Computer Engineering, or related field. 8+ years of experience in embedded systems development with at least 2–3 years in a leadership or management role. Strong proficiency in C/C++, RTOS, and embedded Linux environments. Experience with microcontrollers (ARM Cortex-M, etc.) and SoC platforms. Familiarity with communication protocols: BLE, Wi-Fi, MQTT, Zigbee, LoRa, etc. Solid understanding of secure coding practices and embedded security (e.g., secure boot, encryption). Experience in IoT product development from prototype to production. Strong interpersonal and communication skills. Preferred: Experience working with cloud-connected devices (AWS IoT, Azure IoT, etc.). Knowledge of hardware debugging tools (oscilloscopes, logic analyzers, JTAG). Familiarity with regulatory compliance (e.g., FCC, CE) and certifications. Agile/Scrum experience and familiarity with tools like Jira, Git, Jenkins.

Skilled IoT Developer with experience in embedded systems, expertise in C/C++ programming and Arduino-based development and testing. You will work closely with our R&D teams to build and deploy IoT automation systems for petrol pumps.

Preferred candidate profile 1. LLM Basics : (Llama, Gemini ) : Understand the basics of generative AI and LLMs, such as key terminology, uses, potential issues, and primary frameworks. One should know what the data is trained on and any potential biases/issues that there may be with the data . Knowledge on know exactly how big LLMs can be, how computationally expensive training will be, and the differences between training LLMs and machine learning models. 1. Prompt Engineering : Knowledge on designing inputs for LLMs once theyre developed. 2. Prompt Engineering with OpenAI : As a leading figure in LLMs and generative AI, it’s important to know how to use prompt engineering specifically with OpenAI tools, as you’ll likely be using them at some point in your career. 3. Question-Answering :Question-answering (QA) LLMs are a type of large language model that has been trained specifically to answer questions. 4. Fine-Tuning : Knowledge on Fine-tuning to improve the performance of an LLM on a variety of tasks, including text generation, translation, summarization, and question-answering. Customize LLMs for specific applications, such as customer service chatbots or medical diagnosis systems. Awareness on supervised learning. This involves providing the LLM with a dataset of labelled data, where each data point is a pair of input and output.. 1. Lang Chain : To architect complex LLM pipelines by chaining multiple models together (Classification, text generation, code generation, etc.)`Agents` to interact with all these external systems to execute actions dictated by LLMs. 2. Parameter Efficiency/Tuning : LORA 3. RAG Building : Generative AI, mastering RAG building—short for Retrieval-Augmented Generation—is becoming increasingly crucial 4. ML OPS and in particular LLMOps : Large Language Model Operations, is the practice of managing and maintaining large language models (LLMs) in a production setting 5. TensorFlow i s like a versatile toolbox for creating intelligent programs that can learn and understand various concepts, including machine learning, deep learning, and data science

Role Purpose The purpose of this role is to design, test and maintain software programs for operating systems or applications which needs to be deployed at a client end and ensure its meet 100% quality assurance parameters Machine Learning & Deep Learning – Strong understanding of LLM architectures, transformers, and fine-tuning techniques. MLOps & DevOps – Experience with CI/CD pipelines, model deployment, and monitoring. Vector Databases – Knowledge of storing and retrieving embeddings efficiently. Prompt Engineering – Ability to craft effective prompts for optimal model responses. Retrieval-Augmented Generation (RAG) – Implementing techniques to enhance LLM outputs with external knowledge. Cloud Platforms – Familiarity with AWS, Azure, or GCP for scalable deployments. Containerization & Orchestration – Using Docker and Kubernetes for model deployment. Observability & Monitoring – Tracking model performance, latency, and drift. Security & Ethics – Ensuring responsible AI practices and data privacy. Programming Skills – Strong proficiency in Python, SQL, and API development. Knowledge of Open-Source LLMs – Familiarity with models like LLaMA, Falcon, and Mistral. Fine-Tuning & Optimization – Experience with LoRA, quantization, and efficient training techniques. LLM Frameworks – Hands-on experience with Hugging Face, LangChain, or OpenAI APIs. Data Engineering – Understanding of ETL pipelines and data preprocessing. Microservices Architecture – Ability to design scalable AI-powered applications. Explainability & Interpretability – Techniques for understanding and debugging LLM outputs. Graph Databases – Knowledge of Neo4j or similar technologies for complex data relationships. Collaboration & Communication – Ability to work with cross-functional teams and explain technical concepts clearly. Deliver No. Performance Parameter Measure 1. Continuous Integration, Deployment & Monitoring of Software 100% error free on boarding & implementation, throughput %, Adherence to the schedule/ release plan 2. Quality & CSAT On-Time Delivery, Manage software, Troubleshoot queries, Customer experience, completion of assigned certifications for skill upgradation 3. MIS & Reporting 100% on time MIS & report generation Mandatory Skills: LLM Ops. Experience3-5 Years.

Senior Technical Architect Machine Learning Solutions We are looking for a Senior Technical Architect with deep expertise in Machine Learning (ML), Artificial Intelligence (AI) , and scalable ML system design . This role will focus on leading the end-to-end architecture of advanced ML-driven platforms, delivering impactful, production-grade AI solutions across the enterprise. Key Responsibilities Lead the architecture and design of enterprise-grade ML platforms , including data pipelines, model training pipelines, model inference services, and monitoring frameworks. Architect and optimize ML lifecycle management systems (MLOps) to support scalable, reproducible, and secure deployment of ML models in production. Design and implement retrieval-augmented generation (RAG) systems, vector databases , semantic search , and LLM orchestration frameworks (e.g., LangChain, Autogen). Define and enforce best practices in model development, versioning, CI/CD pipelines , model drift detection, retraining, and rollback mechanisms. Build robust pipelines for data ingestion, preprocessing, feature engineering , and model training at scale , using batch and real-time streaming architectures. Architect multi-modal ML solutions involving NLP, computer vision, time-series, or structured data use cases. Collaborate with data scientists, ML engineers, DevOps, and product teams to convert research prototypes into scalable production services . Implement observability for ML models including custom metrics, performance monitoring, and explainability (XAI) tooling. Evaluate and integrate third-party LLMs (e.g., OpenAI, Claude, Cohere) or open-source models (e.g., LLaMA, Mistral) as part of intelligent application design. Create architectural blueprints and reference implementations for LLM APIs, model hosting, fine-tuning, and embedding pipelines . Guide the selection of compute frameworks (GPUs, TPUs), model serving frameworks (e.g., TorchServe, Triton, BentoML) , and scalable inference strategies (batch, real-time, streaming). Drive AI governance and responsible AI practices including auditability, compliance, bias mitigation, and data protection. Stay up to date on the latest developments in ML frameworks, foundation models, model compression, distillation, and efficient inference . 14. Ability to coach and lead technical teams , fostering growth, knowledge sharing, and technical excellence in AI/ML domains. Experience managing the technical roadmap for AI-powered products , documentations ensuring timely delivery, performance optimization, and stakeholder alignment. Required Qualifications Bachelors or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field. 8+ years of experience in software architecture , with 5+ years focused specifically on machine learning systems and 2 years in leading team. Proven expertise in designing and deploying ML systems at scale , across cloud and hybrid environments. Strong hands-on experience with ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face, Scikit-learn). Experience with vector databases (e.g., FAISS, Pinecone, Weaviate, Qdrant) and embedding models (e.g., SBERT, OpenAI, Cohere). Demonstrated proficiency in MLOps tools and platforms : MLflow, Kubeflow, SageMaker, Vertex AI, DataBricks, Airflow, etc. In-depth knowledge of cloud AI/ML services on AWS, Azure, or GCP – including certification(s) in one or more platforms. Experience with containerization and orchestration (Docker, Kubernetes) for model packaging and deployment. Ability to design LLM-based systems , including hybrid models (open-source + proprietary), fine-tuning strategies, and prompt engineering. Solid understanding of security, compliance , and AI risk management in ML deployments. Preferred Skills Experience with AutoML , hyperparameter tuning, model selection, and experiment tracking. Knowledge of LLM tuning techniques : LoRA, PEFT, quantization, distillation, and RLHF. Knowledge of privacy-preserving ML techniques , federated learning, and homomorphic encryption Familiarity with zero-shot, few-shot learning , and retrieval-enhanced inference pipelines. Contributions to open-source ML tools or libraries. Experience deploying AI copilots, agents, or assistants using orchestration frameworks.

Role & responsibilities End-to-end IoT product design and development of hardware, PCB designing, board architecture, complete knowledge of components and integration. In-depth knowledge of Sensors and other hardware components, functionality, technical and integration with other components and firmware for desired performance. Should be able to work with firmware and software teams to develop the solution. Should have developed at least 4 IoT hardware solutions starting from PCB designing, components integration, firmware integration, API integration and complete product. Complete knowledge of hardware (PCB, Components, Sensors), Firmware, cloud, networking, protocols (MQTT, CoAP, LoRa, Wi-Fi, Bluetooth) Work from Hyderabad Office.

Min1+ Yrs exp in RF& microwave engineering Exp in RF design tools(HFSS,CST,ADS,Ansys) Exp in circuit design & simulation tools MATLAB,Python,SPICE,RF PCB design,layout considerations,EMI/EMC mitigation Knowledge in communication standards & protocols

will be responsible for designing, developing, testing, and optimizing embedded software and hardware systems. You will play a key role in creating solutions that are efficient, reliable, and scalable across various embedded platforms. Required Candidate profile 2+ years of exp in embedded software development Strong proficiency in C and/or C++ for embedded systems Hands-on experience with RTOS, bare-metal programming & device drivers Prefer immediate joiner

Job Title: AI Engineer / AI Visualizer Work Location: Salem, Tamil Nadu (Hybrid) About Us: BuildingWorld is a stealth-mode startup quietly building a marketplace aimed at reshape transforming Indias construction ecosystem. BuildingWorld is backed by the strategic guidance of Mukesh & Associates (www.mukeshassociates.com) If you're interested, please send your updated CV to sumathi@mukeshassociates.com. Role Overview: We are looking for a creative and technically adept AI Visualizer with deep expertise in Computer Vision and Generative AI . The ideal candidate will have hands-on experience with Stable Diffusion, SDXL, Flux, ControlNet, and LoRA , and a strong foundation in deep learning frameworks and image processing tools . This role blends artistic innovation with engineering precision to push the boundaries of AI-generated visual content. Key Responsibilities: Fine-tune and deploy generative models ( Stable Diffusion, SDXL, ControlNet, LoRA ) Implement inpainting, outpainting, style transfer , and dynamic adapter loading Build and maintain Inference APIs for image generation workflows Integrate upscaling models (Real-ESRGAN, SwinIR) to enhance image quality Apply content safety filters for NSFW/violent imagery Optimize GPU usage and model caching for faster inference Deploy solutions via cloud platforms using Docker & Kubernetes Required Skills & Qualifications: 2+ years in Computer Vision and Machine Learning Proficient in Python , OpenCV , and image processing libraries Experience with TensorFlow, PyTorch, or Keras Skilled in model fine-tuning and AI-driven visual applications Familiar with REST/GraphQL APIs , SQL/NoSQL databases Cloud deployment experience on AWS, Azure, or GCP Proficient in Docker, Kubernetes , and CI/CD workflows

Are you obsessed with pushing the boundaries of Generative AI? Have you fine tuned LLaMA models, dived into LoRA or even full-scale fine tuning? Then this opportunity is tailor-made for you ! We are on the hunt for AI /ML Engineers/ Research Engineers /ML Developers with Strong experience in LLM Fine tuning and python to join our AI innovation team. Location : Siruseri, Chennai, Experience : 2 to 10 Years Notice Period : Immediate Joiners to 30 Days Role & responsibilities : Fine-tune LLaMA models on domain-specific data (e.g., finance, healthcare, telecom, legal, etc.) Curate, clean, and pre-process datasets for supervised and unsupervised fine-tuning. Implement low-rank adaptation (LoRA), PEFT, QLoRA, or full fine-tuning as per need. Optimize model training performance using tools/libraries. Evaluate fine-tuned models using appropriate metrics. Deploy and integrate models with APIs, RAG pipelines, or inference servers. Use tools like Weights & Biases, LangSmith, or TensorBoard for training monitoring and logging. Conduct safety audits and hallucination checks post-fine-tuning. Familiarity with open-source LLMs beyond LLaMA (Mistral, Falcon, Mixtral, etc.). Hands-on with orchestration tools like LangChain, LangGraph, CrewAI, or Flowise. Knowledge of tokenizers, embeddings, and prompt templates. Experience with LLaMA (preferably LLaMA 2 / LLaMA 3) and Hugging Face ecosystem. Proficiency in Python, PyTorch, and model training workflows. what are we looking for ? Deep expertise in Fine tuning in LLama, LoRA, QLoRA & strong command of Python & ML Libraries Why join us? Work with Top tier AI minds Get access to high performance infra for model training Drive innovation with real world applications Be part of a growing AI team building next -gen tools. Nonstop learning and innovation Competitive salary + growth opportunities Ready to build the future with LLM'S? Apply now and shape the future of AI ! Tag your resume to Latha.s@excelacom.in / +91 7667620910 and GitHub with your latest fine- tuning experiments . Lets build the future together - one fine- tuned model at a time.

Hinduja Global Solutions (HGS) is a global company specializing in business process management (BPM) and digital customer experience (CX) solutions. HGS offers a range of services including contact center solutions, back-office processing, and integrated digital platforms. They are part of the Hinduja Group, a large multinational conglomerate. With over 20,000 employees across 34 delivery centers in 8 countries, HGS offers a spectrum of technology, digital marketing, and outsourcing solutions tailored to specific industry verticals. What we are looking for: Core Skills & Knowledge Programming: Expert Python; deep use of Pandas, NumPy, Scikit-learn, PyTorch/Lightning; strong OO and functional patterns. Large Language Models: Fine-tuning (LoRA, QLoRA, PEFT), prompt engineering, evaluation (BLEU, BERTScore, hallucination metrics). Machine Learning: Classical algorithms (tree-based, linear, ensemble, time-series), hyper-parameter search (Optuna, Ray Tune), model explainability (SHAP, Captum). Statistical Methods: Experimental design, p-value and power analysis, Bayesian A/B testing, causal inference. Vector Search & RAG: Embedding generation, similarity search tuning (HNSW, IVF-PQ), chunking strategies, knowledge-base hygiene. DevOps & Tooling: Git, Docker, Conda/Poetry, make/Task, notebook versioning, automated linting & unit tests (pytest). Cloud & Data Platforms: Familiarity with AWS (SageMaker, Bedrock) or Azure (ML, OpenAI) plus modern data warehouses (Snowflake, Redshift, BigQuery) and lake formats (Parquet, Delta). Why this role matters We deliver data-driven and GenAI-powered products that move business KPIs, not just models. Youll transform raw data and cutting-edge research (LLMs, causal inference, RAG pipelines) into production-grade capabilities that customers use every day. Key Responsibilities Responsibility Model development & experimentation – Build and validate supervised/unsupervised models (classification, regression, clustering) and LLM fine-tuning pipelines; design statistically rigorous experiments (A/B, multivariate, uplift). This also includes RAG applications, and tune RAG workflows with vector databases (pgvector, Milvus) and embeddings (OpenAI, Hugging Face). Optimize retrieval recall, latency, and hallucination rates. Causal & statistical analysis – Apply quasi-experimental methods (DID, propensity scoring, synthetic controls) and hypothesis tests to uncover drivers of business outcomes. Operationalization & MLOps – Package models as reproducible artifacts (MLflow, TorchScript, BentoML), write CI/CD tests, and partner with MLOps engineers on scalable deployment and monitoring. Data wrangling & feature engineering – Build robust ETL/ELT pipelines in Python (Pandas, Polars, SQL) and orchestrate workflows (Airflow, Dagster). Collaboration & knowledge sharing – Translate findings to non-technical stakeholders, contribute to shared notebooks, mentor junior analysts, and document best practices. Nice-to-Have / Stretch Skills Graph data science (Neo4j, Graph-SAGE) Streaming ML on Apache Kafka / Flink LangChain / LlamaIndex orchestration Real-time feature stores (Feast, Tecton) Domain knowledge in finance, healthcare, or supply chain Experience & Education 6-10 years of overall IT experience with over 5 years of relevant experience building and shipping ML models or GenAI services in production. Proven track record of improving a business metric (conversion, churn, NPS, cost) through data science. Bachelor’s or Master’s in Computer Science, Statistics, Mathematics, or related field (or equivalent hands-on experience). Working at HGS: At TekLink our employees get an open and collaborative environment and gain experience working for customers in various industries while solving complex business problems. They get support to learn new technologies as well as to enhance existing skills to further their career growth. We offer: Very competitive remuneration Excellent Benefits including – Hot Skills allowance, Shift allowance, Health, accidental and Life coverage. Excellent performance based annual increments. Fast paced growth opportunities International work experience Opportunity to participate in various sports and CSR activities.